{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XGBoost" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Team members:\n", "* Technical writer - **Abylay Aitbanov**\n", "* Author of executable content - **Alisher Aip**\n", "* Project Manager - **Adilzhan Jumakanov**" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Book about XGBoost\n", " Read here \n", "
\n", "\"Description" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "![](assets/img_3.png)\n", "\n", "[XGBoost](https://github.com/dmlc/xgboost) is one of the most popular and efficient implementations of the Gradient Boosted Trees algorithm, a supervised learning method that is based on function approximation by optimizing specific loss functions as well as applying several regularization techniques. It is an ensemble learning method that combines the predictions of multiple weak models to produce a stronger prediction. \n", "\n", "XGBoost stands for “Extreme Gradient Boosting” and it has become one of the most popular and widely used machine learning algorithms due to its ability to handle large datasets and its ability to achieve state-of-the-art performance in many machine learning tasks such as classification and regression." ] }, { "cell_type": "markdown", "source": [ "
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" ], "metadata": { "tags": [ "remove-input" ], "collapsed": false } }, { "cell_type": "code", "execution_count": 390, "outputs": [ { "data": { "text/plain": "", "text/html": "
" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": "", "application/javascript": "var element=document.getElementById(\"q_demo_seq\");\n var questionsdfHbuFGyJzlD;\n try {\n questionsdfHbuFGyJzlD=JSON.parse(window.atob(element.innerHTML));\n } catch(err) {\n console.log(\"Fell into catch\");\n questionsdfHbuFGyJzlD = JSON.parse(element.innerHTML);\n }\n console.log(questionsdfHbuFGyJzlD);;\n // Make a random ID\nfunction makeid(length) {\n var result = [];\n var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n var charactersLength = characters.length;\n for (var i = 0; i < length; i++) {\n result.push(characters.charAt(Math.floor(Math.random() * charactersLength)));\n }\n return result.join('');\n}\n\n// Choose a random subset of an array. Can also be used to shuffle the array\nfunction getRandomSubarray(arr, size) {\n var shuffled = arr.slice(0), i = arr.length, temp, index;\n while (i--) {\n index = Math.floor((i + 1) * Math.random());\n temp = shuffled[index];\n shuffled[index] = shuffled[i];\n shuffled[i] = temp;\n }\n return shuffled.slice(0, size);\n}\n\nfunction printResponses(responsesContainer) {\n var responses=JSON.parse(responsesContainer.dataset.responses);\n var stringResponses='IMPORTANT!To preserve this answer sequence for submission, when you have finalized your answers:
  1. Copy the text in this cell below \"Answer String\"
  2. Double click on the cell directly below the Answer String, labeled \"Replace Me\"
  3. Select the whole \"Replace Me\" text
  4. Paste in your answer string and press shift-Enter.
  5. Save the notebook using the save icon or File->Save Notebook menu item



    6. Answer String:
    ';\n console.log(responses);\n responses.forEach((response, index) => {\n if (response) {\n console.log(index + ': ' + response);\n stringResponses+= index + ': ' + response +\"
    \";\n }\n });\n responsesContainer.innerHTML=stringResponses;\n}\nfunction check_mc() {\n var id = this.id.split('-')[0];\n //var response = this.id.split('-')[1];\n //console.log(response);\n //console.log(\"In check_mc(), id=\"+id);\n //console.log(event.srcElement.id) \n //console.log(event.srcElement.dataset.correct) \n //console.log(event.srcElement.dataset.feedback)\n\n var label = event.srcElement;\n //console.log(label, label.nodeName);\n var depth = 0;\n while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n label = label.parentElement;\n console.log(depth, label);\n depth++;\n }\n\n\n\n var answers = label.parentElement.children;\n\n //console.log(answers);\n\n\n // Split behavior based on multiple choice vs many choice:\n var fb = document.getElementById(\"fb\" + id);\n\n\n\n\n if (fb.dataset.numcorrect == 1) {\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n //console.log(responsesContainer);\n var response = label.firstChild.innerText;\n if (label.querySelector(\".QuizCode\")){\n response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n }\n console.log(response);\n //console.log(document.getElementById(\"quizWrap\"+id));\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n console.log(responses);\n responses[qnum]= response;\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End code to preserve responses\n \n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n //console.log(child);\n child.className = \"MCButton\";\n }\n\n\n\n if (label.dataset.correct == \"true\") {\n // console.log(\"Correct action\");\n if (\"feedback\" in label.dataset) {\n fb.textContent = jaxify(label.dataset.feedback);\n } else {\n fb.textContent = \"Correct!\";\n }\n label.classList.add(\"correctButton\");\n\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n\n } else {\n if (\"feedback\" in label.dataset) {\n fb.textContent = jaxify(label.dataset.feedback);\n } else {\n fb.textContent = \"Incorrect -- try again.\";\n }\n //console.log(\"Error action\");\n label.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n }\n else {\n var reset = false;\n var feedback;\n if (label.dataset.correct == \"true\") {\n if (\"feedback\" in label.dataset) {\n feedback = jaxify(label.dataset.feedback);\n } else {\n feedback = \"Correct!\";\n }\n if (label.dataset.answered <= 0) {\n if (fb.dataset.answeredcorrect < 0) {\n fb.dataset.answeredcorrect = 1;\n reset = true;\n } else {\n fb.dataset.answeredcorrect++;\n }\n if (reset) {\n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n child.className = \"MCButton\";\n child.dataset.answered = 0;\n }\n }\n label.classList.add(\"correctButton\");\n label.dataset.answered = 1;\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n\n }\n } else {\n if (\"feedback\" in label.dataset) {\n feedback = jaxify(label.dataset.feedback);\n } else {\n feedback = \"Incorrect -- try again.\";\n }\n if (fb.dataset.answeredcorrect > 0) {\n fb.dataset.answeredcorrect = -1;\n reset = true;\n } else {\n fb.dataset.answeredcorrect--;\n }\n\n if (reset) {\n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n child.className = \"MCButton\";\n child.dataset.answered = 0;\n }\n }\n label.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n //console.log(responsesContainer);\n var response = label.firstChild.innerText;\n if (label.querySelector(\".QuizCode\")){\n response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n }\n console.log(response);\n //console.log(document.getElementById(\"quizWrap\"+id));\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n if (label.dataset.correct == \"true\") {\n if (typeof(responses[qnum]) == \"object\"){\n if (!responses[qnum].includes(response))\n responses[qnum].push(response);\n } else{\n responses[qnum]= [ response ];\n }\n } else {\n responses[qnum]= response;\n }\n console.log(responses);\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End save responses stuff\n\n\n\n var numcorrect = fb.dataset.numcorrect;\n var answeredcorrect = fb.dataset.answeredcorrect;\n if (answeredcorrect >= 0) {\n fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n } else {\n fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n }\n\n\n }\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.version;\n console.log('MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([fb]);\n }\n } else {\n console.log('MathJax not detected');\n }\n\n}\n\nfunction make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n var shuffled;\n if (shuffle_answers == \"True\") {\n //console.log(shuffle_answers+\" read as true\");\n shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n } else {\n //console.log(shuffle_answers+\" read as false\");\n shuffled = qa.answers;\n }\n\n\n var num_correct = 0;\n\n\n\n shuffled.forEach((item, index, ans_array) => {\n //console.log(answer);\n\n // Make input element\n var inp = document.createElement(\"input\");\n inp.type = \"radio\";\n inp.id = \"quizo\" + id + index;\n inp.style = \"display:none;\";\n aDiv.append(inp);\n\n //Make label for input element\n var lab = document.createElement(\"label\");\n lab.className = \"MCButton\";\n lab.id = id + '-' + index;\n lab.onclick = check_mc;\n var aSpan = document.createElement('span');\n aSpan.classsName = \"\";\n //qDiv.id=\"quizQn\"+id+index;\n if (\"answer\" in item) {\n aSpan.innerHTML = jaxify(item.answer);\n //aSpan.innerHTML=item.answer;\n }\n lab.append(aSpan);\n\n // Create div for code inside question\n var codeSpan;\n if (\"code\" in item) {\n codeSpan = document.createElement('span');\n codeSpan.id = \"code\" + id + index;\n codeSpan.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeSpan.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = item.code;\n lab.append(codeSpan);\n //console.log(codeSpan);\n }\n\n //lab.textContent=item.answer;\n\n // Set the data attributes for the answer\n lab.setAttribute('data-correct', item.correct);\n if (item.correct) {\n num_correct++;\n }\n if (\"feedback\" in item) {\n lab.setAttribute('data-feedback', item.feedback);\n }\n lab.setAttribute('data-answered', 0);\n\n aDiv.append(lab);\n\n });\n\n if (num_correct > 1) {\n outerqDiv.className = \"ManyChoiceQn\";\n } else {\n outerqDiv.className = \"MultipleChoiceQn\";\n }\n\n return num_correct;\n\n}\nfunction check_numeric(ths, event) {\n\n if (event.keyCode === 13) {\n ths.blur();\n\n var id = ths.id.split('-')[0];\n\n var submission = ths.value;\n if (submission.indexOf('/') != -1) {\n var sub_parts = submission.split('/');\n //console.log(sub_parts);\n submission = sub_parts[0] / sub_parts[1];\n }\n //console.log(\"Reader entered\", submission);\n\n if (\"precision\" in ths.dataset) {\n var precision = ths.dataset.precision;\n // console.log(\"1:\", submission)\n submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n // console.log(\"Rounded to \", submission, \" precision=\", precision );\n }\n\n\n //console.log(\"In check_numeric(), id=\"+id);\n //console.log(event.srcElement.id) \n //console.log(event.srcElement.dataset.feedback)\n\n var fb = document.getElementById(\"fb\" + id);\n fb.style.display = \"none\";\n fb.textContent = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n //console.log(answers);\n\n var defaultFB = \"\";\n var correct;\n var done = false;\n answers.every(answer => {\n //console.log(answer.type);\n\n correct = false;\n // if (answer.type==\"value\"){\n if ('value' in answer) {\n if (submission == answer.value) {\n if (\"feedback\" in answer) {\n fb.textContent = jaxify(answer.feedback);\n } else {\n fb.textContent = jaxify(\"Correct\");\n }\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n // } else if (answer.type==\"range\") {\n } else if ('range' in answer) {\n //console.log(answer.range);\n if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n fb.textContent = jaxify(answer.feedback);\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n } else if (answer.type == \"default\") {\n defaultFB = answer.feedback;\n }\n if (done) {\n return false; // Break out of loop if this has been marked correct\n } else {\n return true; // Keep looking for case that includes this as a correct answer\n }\n });\n\n if ((!done) && (defaultFB != \"\")) {\n fb.innerHTML = jaxify(defaultFB);\n //console.log(\"Default feedback\", defaultFB);\n }\n\n fb.style.display = \"block\";\n if (correct) {\n ths.className = \"Input-text\";\n ths.classList.add(\"correctButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n } else {\n ths.className = \"Input-text\";\n ths.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n console.log(submission);\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n //console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n console.log(responses);\n if (submission == ths.value){\n responses[qnum]= submission;\n } else {\n responses[qnum]= ths.value + \"(\" + submission +\")\";\n }\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End code to preserve responses\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.version;\n console.log('MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([fb]);\n }\n } else {\n console.log('MathJax not detected');\n }\n return false;\n }\n\n}\n\nfunction isValid(el, charC) {\n //console.log(\"Input char: \", charC);\n if (charC == 46) {\n if (el.value.indexOf('.') === -1) {\n return true;\n } else if (el.value.indexOf('/') != -1) {\n var parts = el.value.split('/');\n if (parts[1].indexOf('.') === -1) {\n return true;\n }\n }\n else {\n return false;\n }\n } else if (charC == 47) {\n if (el.value.indexOf('/') === -1) {\n if ((el.value != \"\") && (el.value != \".\")) {\n return true;\n } else {\n return false;\n }\n } else {\n return false;\n }\n } else if (charC == 45) {\n var edex = el.value.indexOf('e');\n if (edex == -1) {\n edex = el.value.indexOf('E');\n }\n\n if (el.value == \"\") {\n return true;\n } else if (edex == (el.value.length - 1)) { // If just after e or E\n return true;\n } else {\n return false;\n }\n } else if (charC == 101) { // \"e\"\n if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n // Prev symbol must be digit or decimal point:\n if (el.value.slice(-1).search(/\\d/) >= 0) {\n return true;\n } else if (el.value.slice(-1).search(/\\./) >= 0) {\n return true;\n } else {\n return false;\n }\n } else {\n return false;\n }\n } else {\n if (charC > 31 && (charC < 48 || charC > 57))\n return false;\n }\n return true;\n}\n\nfunction numeric_keypress(evnt) {\n var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n\n if (charC == 13) {\n check_numeric(this, evnt);\n } else {\n return isValid(this, charC);\n }\n}\n\n\n\n\n\nfunction make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n\n\n\n //console.log(answer);\n\n\n outerqDiv.className = \"NumericQn\";\n aDiv.style.display = 'block';\n\n var lab = document.createElement(\"label\");\n lab.className = \"InpLabel\";\n lab.textContent = \"Type numeric answer here:\";\n aDiv.append(lab);\n\n var inp = document.createElement(\"input\");\n inp.type = \"text\";\n //inp.id=\"input-\"+id;\n inp.id = id + \"-0\";\n inp.className = \"Input-text\";\n inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n if (\"precision\" in qa) {\n inp.setAttribute('data-precision', qa.precision);\n }\n aDiv.append(inp);\n //console.log(inp);\n\n //inp.addEventListener(\"keypress\", check_numeric);\n //inp.addEventListener(\"keypress\", numeric_keypress);\n /*\n inp.addEventListener(\"keypress\", function(event) {\n return numeric_keypress(this, event);\n }\n );\n */\n //inp.onkeypress=\"return numeric_keypress(this, event)\";\n inp.onkeypress = numeric_keypress;\n inp.onpaste = event => false;\n\n inp.addEventListener(\"focus\", function (event) {\n this.value = \"\";\n return false;\n }\n );\n\n\n}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([mydiv]);\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([mydiv]);\n } else {\n console.log(\"MathJax not found\");\n }\n }\n }\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"dfHbuFGyJzlD\")) {\n show_questions(questionsdfHbuFGyJzlD, dfHbuFGyJzlD); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"dfHbuFGyJzlD\");\n // console.log(document.getElementById(\"dfHbuFGyJzlD\"));\n\n try_show();\n }\n " }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from jupyterquiz import display_quiz\n", "display_quiz(\"#q_demo_seq\")" ], "metadata": { "tags": [ "remove-input" ], "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:57.619604Z", "start_time": "2023-12-15T05:24:57.483428Z" } } }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "#### Objective Function in XGBoost" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "The objective function in XGBoost is designed to be minimized during the training process. For a regression problem, it is commonly defined as follows:\n", "\n", "$$\n", "\\text{Objective} = \\sum_{i=1}^{n} L(y_i, \\hat{y}_i) + \\sum_{k=1}^{K} \\Omega(f_k)\n", "$$\n", "\n", "Here's a breakdown of the components:\n", "\n", "\n", "$$ \\sum_{i=1}^{n} L(y_i, \\hat{y}_i) $$\n", " - This term represents the sum of the loss function over all data points.\n", " - \\(n\\) is the number of data points.\n", " - $ L(y_i, \\hat{y}_i) $ is the loss incurred for predicting $ \\hat{y}_i $ when the true label is $ y_i $.\n", "\n", " \n", "$$ \\sum_{k=1}^{K} \\Omega(f_k) $$\n", " - This term represents the sum of the regularization terms over all the trees in the ensemble.\n", " - $ K $ is the number of trees.\n", " - $ f_k $ - a function (in our case a tree) that we want to train at the step\n", " - $ \\Omega(f_k) $ is the regularization term for the $ k $-th tree.\n", "\n", " \n", "For a regression problem, a common choice for the loss function is the mean squared error (MSE), which is given by:\n", "\n", "$$\n", "L(y_i, \\hat{y}_i) = (y_i - \\hat{y}_i)^2\n", "$$\n", "\n", "\n", "The regularization term $ \\Omega(f_k) $ typically consists of two parts:\n", "\n", "\n", "$$\n", "\\Omega(f_k) = \\gamma T + \\frac{1}{2}\\lambda \\sum_{j=1}^{T} w_{j}^2\n", "$$\n", "\n", "Here:\n", "- $ T $ is the number of leaves in the tree.\n", "- $ w_j $ is the score assigned to the \\(j\\)-th leaf.\n", "- $ \\gamma\\ $ and $ \\lambda\\ $ are regularization parameters.\n", "\n", "The entire objective function is designed to balance the model's fit to the training data (captured by the loss function) with a penalty for complexity (captured by the regularization term). It ensures that the model generalizes well to unseen data while avoiding overfitting." ] }, { "cell_type": "markdown", "source": [ "
    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
    " ], "metadata": { "tags": [ "remove-input" ], "collapsed": false } }, { "cell_type": "code", "execution_count": 391, "outputs": [ { "data": { "text/plain": "", "text/html": "
    " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": "", "application/javascript": "var element=document.getElementById(\"q_demo_seq2\");\n var questionscqizutkAfWjz;\n try {\n questionscqizutkAfWjz=JSON.parse(window.atob(element.innerHTML));\n } catch(err) {\n console.log(\"Fell into catch\");\n questionscqizutkAfWjz = JSON.parse(element.innerHTML);\n }\n console.log(questionscqizutkAfWjz);;\n // Make a random ID\nfunction makeid(length) {\n var result = [];\n var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n var charactersLength = characters.length;\n for (var i = 0; i < length; i++) {\n result.push(characters.charAt(Math.floor(Math.random() * charactersLength)));\n }\n return result.join('');\n}\n\n// Choose a random subset of an array. Can also be used to shuffle the array\nfunction getRandomSubarray(arr, size) {\n var shuffled = arr.slice(0), i = arr.length, temp, index;\n while (i--) {\n index = Math.floor((i + 1) * Math.random());\n temp = shuffled[index];\n shuffled[index] = shuffled[i];\n shuffled[i] = temp;\n }\n return shuffled.slice(0, size);\n}\n\nfunction printResponses(responsesContainer) {\n var responses=JSON.parse(responsesContainer.dataset.responses);\n var stringResponses='IMPORTANT!To preserve this answer sequence for submission, when you have finalized your answers:
    1. Copy the text in this cell below \"Answer String\"
    2. Double click on the cell directly below the Answer String, labeled \"Replace Me\"
    3. Select the whole \"Replace Me\" text
    4. Paste in your answer string and press shift-Enter.
    5. Save the notebook using the save icon or File->Save Notebook menu item



      6. Answer String:
      ';\n console.log(responses);\n responses.forEach((response, index) => {\n if (response) {\n console.log(index + ': ' + response);\n stringResponses+= index + ': ' + response +\"
      \";\n }\n });\n responsesContainer.innerHTML=stringResponses;\n}\nfunction check_mc() {\n var id = this.id.split('-')[0];\n //var response = this.id.split('-')[1];\n //console.log(response);\n //console.log(\"In check_mc(), id=\"+id);\n //console.log(event.srcElement.id) \n //console.log(event.srcElement.dataset.correct) \n //console.log(event.srcElement.dataset.feedback)\n\n var label = event.srcElement;\n //console.log(label, label.nodeName);\n var depth = 0;\n while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n label = label.parentElement;\n console.log(depth, label);\n depth++;\n }\n\n\n\n var answers = label.parentElement.children;\n\n //console.log(answers);\n\n\n // Split behavior based on multiple choice vs many choice:\n var fb = document.getElementById(\"fb\" + id);\n\n\n\n\n if (fb.dataset.numcorrect == 1) {\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n //console.log(responsesContainer);\n var response = label.firstChild.innerText;\n if (label.querySelector(\".QuizCode\")){\n response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n }\n console.log(response);\n //console.log(document.getElementById(\"quizWrap\"+id));\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n console.log(responses);\n responses[qnum]= response;\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End code to preserve responses\n \n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n //console.log(child);\n child.className = \"MCButton\";\n }\n\n\n\n if (label.dataset.correct == \"true\") {\n // console.log(\"Correct action\");\n if (\"feedback\" in label.dataset) {\n fb.textContent = jaxify(label.dataset.feedback);\n } else {\n fb.textContent = \"Correct!\";\n }\n label.classList.add(\"correctButton\");\n\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n\n } else {\n if (\"feedback\" in label.dataset) {\n fb.textContent = jaxify(label.dataset.feedback);\n } else {\n fb.textContent = \"Incorrect -- try again.\";\n }\n //console.log(\"Error action\");\n label.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n }\n else {\n var reset = false;\n var feedback;\n if (label.dataset.correct == \"true\") {\n if (\"feedback\" in label.dataset) {\n feedback = jaxify(label.dataset.feedback);\n } else {\n feedback = \"Correct!\";\n }\n if (label.dataset.answered <= 0) {\n if (fb.dataset.answeredcorrect < 0) {\n fb.dataset.answeredcorrect = 1;\n reset = true;\n } else {\n fb.dataset.answeredcorrect++;\n }\n if (reset) {\n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n child.className = \"MCButton\";\n child.dataset.answered = 0;\n }\n }\n label.classList.add(\"correctButton\");\n label.dataset.answered = 1;\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n\n }\n } else {\n if (\"feedback\" in label.dataset) {\n feedback = jaxify(label.dataset.feedback);\n } else {\n feedback = \"Incorrect -- try again.\";\n }\n if (fb.dataset.answeredcorrect > 0) {\n fb.dataset.answeredcorrect = -1;\n reset = true;\n } else {\n fb.dataset.answeredcorrect--;\n }\n\n if (reset) {\n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n child.className = \"MCButton\";\n child.dataset.answered = 0;\n }\n }\n label.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n //console.log(responsesContainer);\n var response = label.firstChild.innerText;\n if (label.querySelector(\".QuizCode\")){\n response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n }\n console.log(response);\n //console.log(document.getElementById(\"quizWrap\"+id));\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n if (label.dataset.correct == \"true\") {\n if (typeof(responses[qnum]) == \"object\"){\n if (!responses[qnum].includes(response))\n responses[qnum].push(response);\n } else{\n responses[qnum]= [ response ];\n }\n } else {\n responses[qnum]= response;\n }\n console.log(responses);\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End save responses stuff\n\n\n\n var numcorrect = fb.dataset.numcorrect;\n var answeredcorrect = fb.dataset.answeredcorrect;\n if (answeredcorrect >= 0) {\n fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n } else {\n fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n }\n\n\n }\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.version;\n console.log('MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([fb]);\n }\n } else {\n console.log('MathJax not detected');\n }\n\n}\n\nfunction make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n var shuffled;\n if (shuffle_answers == \"True\") {\n //console.log(shuffle_answers+\" read as true\");\n shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n } else {\n //console.log(shuffle_answers+\" read as false\");\n shuffled = qa.answers;\n }\n\n\n var num_correct = 0;\n\n\n\n shuffled.forEach((item, index, ans_array) => {\n //console.log(answer);\n\n // Make input element\n var inp = document.createElement(\"input\");\n inp.type = \"radio\";\n inp.id = \"quizo\" + id + index;\n inp.style = \"display:none;\";\n aDiv.append(inp);\n\n //Make label for input element\n var lab = document.createElement(\"label\");\n lab.className = \"MCButton\";\n lab.id = id + '-' + index;\n lab.onclick = check_mc;\n var aSpan = document.createElement('span');\n aSpan.classsName = \"\";\n //qDiv.id=\"quizQn\"+id+index;\n if (\"answer\" in item) {\n aSpan.innerHTML = jaxify(item.answer);\n //aSpan.innerHTML=item.answer;\n }\n lab.append(aSpan);\n\n // Create div for code inside question\n var codeSpan;\n if (\"code\" in item) {\n codeSpan = document.createElement('span');\n codeSpan.id = \"code\" + id + index;\n codeSpan.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeSpan.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = item.code;\n lab.append(codeSpan);\n //console.log(codeSpan);\n }\n\n //lab.textContent=item.answer;\n\n // Set the data attributes for the answer\n lab.setAttribute('data-correct', item.correct);\n if (item.correct) {\n num_correct++;\n }\n if (\"feedback\" in item) {\n lab.setAttribute('data-feedback', item.feedback);\n }\n lab.setAttribute('data-answered', 0);\n\n aDiv.append(lab);\n\n });\n\n if (num_correct > 1) {\n outerqDiv.className = \"ManyChoiceQn\";\n } else {\n outerqDiv.className = \"MultipleChoiceQn\";\n }\n\n return num_correct;\n\n}\nfunction check_numeric(ths, event) {\n\n if (event.keyCode === 13) {\n ths.blur();\n\n var id = ths.id.split('-')[0];\n\n var submission = ths.value;\n if (submission.indexOf('/') != -1) {\n var sub_parts = submission.split('/');\n //console.log(sub_parts);\n submission = sub_parts[0] / sub_parts[1];\n }\n //console.log(\"Reader entered\", submission);\n\n if (\"precision\" in ths.dataset) {\n var precision = ths.dataset.precision;\n // console.log(\"1:\", submission)\n submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n // console.log(\"Rounded to \", submission, \" precision=\", precision );\n }\n\n\n //console.log(\"In check_numeric(), id=\"+id);\n //console.log(event.srcElement.id) \n //console.log(event.srcElement.dataset.feedback)\n\n var fb = document.getElementById(\"fb\" + id);\n fb.style.display = \"none\";\n fb.textContent = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n //console.log(answers);\n\n var defaultFB = \"\";\n var correct;\n var done = false;\n answers.every(answer => {\n //console.log(answer.type);\n\n correct = false;\n // if (answer.type==\"value\"){\n if ('value' in answer) {\n if (submission == answer.value) {\n if (\"feedback\" in answer) {\n fb.textContent = jaxify(answer.feedback);\n } else {\n fb.textContent = jaxify(\"Correct\");\n }\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n // } else if (answer.type==\"range\") {\n } else if ('range' in answer) {\n //console.log(answer.range);\n if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n fb.textContent = jaxify(answer.feedback);\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n } else if (answer.type == \"default\") {\n defaultFB = answer.feedback;\n }\n if (done) {\n return false; // Break out of loop if this has been marked correct\n } else {\n return true; // Keep looking for case that includes this as a correct answer\n }\n });\n\n if ((!done) && (defaultFB != \"\")) {\n fb.innerHTML = jaxify(defaultFB);\n //console.log(\"Default feedback\", defaultFB);\n }\n\n fb.style.display = \"block\";\n if (correct) {\n ths.className = \"Input-text\";\n ths.classList.add(\"correctButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n } else {\n ths.className = \"Input-text\";\n ths.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n console.log(submission);\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n //console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n console.log(responses);\n if (submission == ths.value){\n responses[qnum]= submission;\n } else {\n responses[qnum]= ths.value + \"(\" + submission +\")\";\n }\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End code to preserve responses\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.version;\n console.log('MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([fb]);\n }\n } else {\n console.log('MathJax not detected');\n }\n return false;\n }\n\n}\n\nfunction isValid(el, charC) {\n //console.log(\"Input char: \", charC);\n if (charC == 46) {\n if (el.value.indexOf('.') === -1) {\n return true;\n } else if (el.value.indexOf('/') != -1) {\n var parts = el.value.split('/');\n if (parts[1].indexOf('.') === -1) {\n return true;\n }\n }\n else {\n return false;\n }\n } else if (charC == 47) {\n if (el.value.indexOf('/') === -1) {\n if ((el.value != \"\") && (el.value != \".\")) {\n return true;\n } else {\n return false;\n }\n } else {\n return false;\n }\n } else if (charC == 45) {\n var edex = el.value.indexOf('e');\n if (edex == -1) {\n edex = el.value.indexOf('E');\n }\n\n if (el.value == \"\") {\n return true;\n } else if (edex == (el.value.length - 1)) { // If just after e or E\n return true;\n } else {\n return false;\n }\n } else if (charC == 101) { // \"e\"\n if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n // Prev symbol must be digit or decimal point:\n if (el.value.slice(-1).search(/\\d/) >= 0) {\n return true;\n } else if (el.value.slice(-1).search(/\\./) >= 0) {\n return true;\n } else {\n return false;\n }\n } else {\n return false;\n }\n } else {\n if (charC > 31 && (charC < 48 || charC > 57))\n return false;\n }\n return true;\n}\n\nfunction numeric_keypress(evnt) {\n var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n\n if (charC == 13) {\n check_numeric(this, evnt);\n } else {\n return isValid(this, charC);\n }\n}\n\n\n\n\n\nfunction make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n\n\n\n //console.log(answer);\n\n\n outerqDiv.className = \"NumericQn\";\n aDiv.style.display = 'block';\n\n var lab = document.createElement(\"label\");\n lab.className = \"InpLabel\";\n lab.textContent = \"Type numeric answer here:\";\n aDiv.append(lab);\n\n var inp = document.createElement(\"input\");\n inp.type = \"text\";\n //inp.id=\"input-\"+id;\n inp.id = id + \"-0\";\n inp.className = \"Input-text\";\n inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n if (\"precision\" in qa) {\n inp.setAttribute('data-precision', qa.precision);\n }\n aDiv.append(inp);\n //console.log(inp);\n\n //inp.addEventListener(\"keypress\", check_numeric);\n //inp.addEventListener(\"keypress\", numeric_keypress);\n /*\n inp.addEventListener(\"keypress\", function(event) {\n return numeric_keypress(this, event);\n }\n );\n */\n //inp.onkeypress=\"return numeric_keypress(this, event)\";\n inp.onkeypress = numeric_keypress;\n inp.onpaste = event => false;\n\n inp.addEventListener(\"focus\", function (event) {\n this.value = \"\";\n return false;\n }\n );\n\n\n}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([mydiv]);\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([mydiv]);\n } else {\n console.log(\"MathJax not found\");\n }\n }\n }\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"cqizutkAfWjz\")) {\n show_questions(questionscqizutkAfWjz, cqizutkAfWjz); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"cqizutkAfWjz\");\n // console.log(document.getElementById(\"cqizutkAfWjz\"));\n\n try_show();\n }\n " }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_quiz(\"#q_demo_seq2\")" ], "metadata": { "tags": [ "remove-input" ], "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:57.623059Z", "start_time": "2023-12-15T05:24:57.490963Z" } } }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "#### Description of the algorithm\n", "\n", "
      \n", "\n", "![](assets/img_2.png)\n", "\n", "XGBoost is based on the gradient boosting algorithm for decision trees. Gradient boosting is a machine learning technique for classification and regression problems that builds a prediction model in the form of an ensemble of weak predictive models, usually decision trees. Ensemble training is carried out sequentially, in contrast to, for example, bagging. \n", "\n", "At each iteration, the deviations of the predictions of the already trained ensemble on the training set are calculated. The next model that will be added to the ensemble will predict these deviations. Thus, by adding the predictions of the new tree to the predictions of the trained ensemble, we can reduce the average deviation of the model, which is the target of the optimization problem. New trees are added to the ensemble until the error decreases or until one of the \"early stopping\" rules is satisfied." ] }, { "cell_type": "markdown", "source": [ "
      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
      " ], "metadata": { "tags": [ "remove-input" ], "collapsed": false } }, { "cell_type": "code", "execution_count": 392, "outputs": [ { "data": { "text/plain": "", "text/html": "
      " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": "", "application/javascript": "var element=document.getElementById(\"q_demo_seq3\");\n var questionsKTvSZwtEYmCM;\n try {\n questionsKTvSZwtEYmCM=JSON.parse(window.atob(element.innerHTML));\n } catch(err) {\n console.log(\"Fell into catch\");\n questionsKTvSZwtEYmCM = JSON.parse(element.innerHTML);\n }\n console.log(questionsKTvSZwtEYmCM);;\n // Make a random ID\nfunction makeid(length) {\n var result = [];\n var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n var charactersLength = characters.length;\n for (var i = 0; i < length; i++) {\n result.push(characters.charAt(Math.floor(Math.random() * charactersLength)));\n }\n return result.join('');\n}\n\n// Choose a random subset of an array. Can also be used to shuffle the array\nfunction getRandomSubarray(arr, size) {\n var shuffled = arr.slice(0), i = arr.length, temp, index;\n while (i--) {\n index = Math.floor((i + 1) * Math.random());\n temp = shuffled[index];\n shuffled[index] = shuffled[i];\n shuffled[i] = temp;\n }\n return shuffled.slice(0, size);\n}\n\nfunction printResponses(responsesContainer) {\n var responses=JSON.parse(responsesContainer.dataset.responses);\n var stringResponses='IMPORTANT!To preserve this answer sequence for submission, when you have finalized your answers:
      1. Copy the text in this cell below \"Answer String\"
      2. Double click on the cell directly below the Answer String, labeled \"Replace Me\"
      3. Select the whole \"Replace Me\" text
      4. Paste in your answer string and press shift-Enter.
      5. Save the notebook using the save icon or File->Save Notebook menu item



        6. Answer String:
        ';\n console.log(responses);\n responses.forEach((response, index) => {\n if (response) {\n console.log(index + ': ' + response);\n stringResponses+= index + ': ' + response +\"
        \";\n }\n });\n responsesContainer.innerHTML=stringResponses;\n}\nfunction check_mc() {\n var id = this.id.split('-')[0];\n //var response = this.id.split('-')[1];\n //console.log(response);\n //console.log(\"In check_mc(), id=\"+id);\n //console.log(event.srcElement.id) \n //console.log(event.srcElement.dataset.correct) \n //console.log(event.srcElement.dataset.feedback)\n\n var label = event.srcElement;\n //console.log(label, label.nodeName);\n var depth = 0;\n while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n label = label.parentElement;\n console.log(depth, label);\n depth++;\n }\n\n\n\n var answers = label.parentElement.children;\n\n //console.log(answers);\n\n\n // Split behavior based on multiple choice vs many choice:\n var fb = document.getElementById(\"fb\" + id);\n\n\n\n\n if (fb.dataset.numcorrect == 1) {\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n //console.log(responsesContainer);\n var response = label.firstChild.innerText;\n if (label.querySelector(\".QuizCode\")){\n response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n }\n console.log(response);\n //console.log(document.getElementById(\"quizWrap\"+id));\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n console.log(responses);\n responses[qnum]= response;\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End code to preserve responses\n \n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n //console.log(child);\n child.className = \"MCButton\";\n }\n\n\n\n if (label.dataset.correct == \"true\") {\n // console.log(\"Correct action\");\n if (\"feedback\" in label.dataset) {\n fb.textContent = jaxify(label.dataset.feedback);\n } else {\n fb.textContent = \"Correct!\";\n }\n label.classList.add(\"correctButton\");\n\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n\n } else {\n if (\"feedback\" in label.dataset) {\n fb.textContent = jaxify(label.dataset.feedback);\n } else {\n fb.textContent = \"Incorrect -- try again.\";\n }\n //console.log(\"Error action\");\n label.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n }\n else {\n var reset = false;\n var feedback;\n if (label.dataset.correct == \"true\") {\n if (\"feedback\" in label.dataset) {\n feedback = jaxify(label.dataset.feedback);\n } else {\n feedback = \"Correct!\";\n }\n if (label.dataset.answered <= 0) {\n if (fb.dataset.answeredcorrect < 0) {\n fb.dataset.answeredcorrect = 1;\n reset = true;\n } else {\n fb.dataset.answeredcorrect++;\n }\n if (reset) {\n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n child.className = \"MCButton\";\n child.dataset.answered = 0;\n }\n }\n label.classList.add(\"correctButton\");\n label.dataset.answered = 1;\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n\n }\n } else {\n if (\"feedback\" in label.dataset) {\n feedback = jaxify(label.dataset.feedback);\n } else {\n feedback = \"Incorrect -- try again.\";\n }\n if (fb.dataset.answeredcorrect > 0) {\n fb.dataset.answeredcorrect = -1;\n reset = true;\n } else {\n fb.dataset.answeredcorrect--;\n }\n\n if (reset) {\n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n child.className = \"MCButton\";\n child.dataset.answered = 0;\n }\n }\n label.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n //console.log(responsesContainer);\n var response = label.firstChild.innerText;\n if (label.querySelector(\".QuizCode\")){\n response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n }\n console.log(response);\n //console.log(document.getElementById(\"quizWrap\"+id));\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n if (label.dataset.correct == \"true\") {\n if (typeof(responses[qnum]) == \"object\"){\n if (!responses[qnum].includes(response))\n responses[qnum].push(response);\n } else{\n responses[qnum]= [ response ];\n }\n } else {\n responses[qnum]= response;\n }\n console.log(responses);\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End save responses stuff\n\n\n\n var numcorrect = fb.dataset.numcorrect;\n var answeredcorrect = fb.dataset.answeredcorrect;\n if (answeredcorrect >= 0) {\n fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n } else {\n fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n }\n\n\n }\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.version;\n console.log('MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([fb]);\n }\n } else {\n console.log('MathJax not detected');\n }\n\n}\n\nfunction make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n var shuffled;\n if (shuffle_answers == \"True\") {\n //console.log(shuffle_answers+\" read as true\");\n shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n } else {\n //console.log(shuffle_answers+\" read as false\");\n shuffled = qa.answers;\n }\n\n\n var num_correct = 0;\n\n\n\n shuffled.forEach((item, index, ans_array) => {\n //console.log(answer);\n\n // Make input element\n var inp = document.createElement(\"input\");\n inp.type = \"radio\";\n inp.id = \"quizo\" + id + index;\n inp.style = \"display:none;\";\n aDiv.append(inp);\n\n //Make label for input element\n var lab = document.createElement(\"label\");\n lab.className = \"MCButton\";\n lab.id = id + '-' + index;\n lab.onclick = check_mc;\n var aSpan = document.createElement('span');\n aSpan.classsName = \"\";\n //qDiv.id=\"quizQn\"+id+index;\n if (\"answer\" in item) {\n aSpan.innerHTML = jaxify(item.answer);\n //aSpan.innerHTML=item.answer;\n }\n lab.append(aSpan);\n\n // Create div for code inside question\n var codeSpan;\n if (\"code\" in item) {\n codeSpan = document.createElement('span');\n codeSpan.id = \"code\" + id + index;\n codeSpan.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeSpan.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = item.code;\n lab.append(codeSpan);\n //console.log(codeSpan);\n }\n\n //lab.textContent=item.answer;\n\n // Set the data attributes for the answer\n lab.setAttribute('data-correct', item.correct);\n if (item.correct) {\n num_correct++;\n }\n if (\"feedback\" in item) {\n lab.setAttribute('data-feedback', item.feedback);\n }\n lab.setAttribute('data-answered', 0);\n\n aDiv.append(lab);\n\n });\n\n if (num_correct > 1) {\n outerqDiv.className = \"ManyChoiceQn\";\n } else {\n outerqDiv.className = \"MultipleChoiceQn\";\n }\n\n return num_correct;\n\n}\nfunction check_numeric(ths, event) {\n\n if (event.keyCode === 13) {\n ths.blur();\n\n var id = ths.id.split('-')[0];\n\n var submission = ths.value;\n if (submission.indexOf('/') != -1) {\n var sub_parts = submission.split('/');\n //console.log(sub_parts);\n submission = sub_parts[0] / sub_parts[1];\n }\n //console.log(\"Reader entered\", submission);\n\n if (\"precision\" in ths.dataset) {\n var precision = ths.dataset.precision;\n // console.log(\"1:\", submission)\n submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n // console.log(\"Rounded to \", submission, \" precision=\", precision );\n }\n\n\n //console.log(\"In check_numeric(), id=\"+id);\n //console.log(event.srcElement.id) \n //console.log(event.srcElement.dataset.feedback)\n\n var fb = document.getElementById(\"fb\" + id);\n fb.style.display = \"none\";\n fb.textContent = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n //console.log(answers);\n\n var defaultFB = \"\";\n var correct;\n var done = false;\n answers.every(answer => {\n //console.log(answer.type);\n\n correct = false;\n // if (answer.type==\"value\"){\n if ('value' in answer) {\n if (submission == answer.value) {\n if (\"feedback\" in answer) {\n fb.textContent = jaxify(answer.feedback);\n } else {\n fb.textContent = jaxify(\"Correct\");\n }\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n // } else if (answer.type==\"range\") {\n } else if ('range' in answer) {\n //console.log(answer.range);\n if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n fb.textContent = jaxify(answer.feedback);\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n } else if (answer.type == \"default\") {\n defaultFB = answer.feedback;\n }\n if (done) {\n return false; // Break out of loop if this has been marked correct\n } else {\n return true; // Keep looking for case that includes this as a correct answer\n }\n });\n\n if ((!done) && (defaultFB != \"\")) {\n fb.innerHTML = jaxify(defaultFB);\n //console.log(\"Default feedback\", defaultFB);\n }\n\n fb.style.display = \"block\";\n if (correct) {\n ths.className = \"Input-text\";\n ths.classList.add(\"correctButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n } else {\n ths.className = \"Input-text\";\n ths.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n console.log(submission);\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n //console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n console.log(responses);\n if (submission == ths.value){\n responses[qnum]= submission;\n } else {\n responses[qnum]= ths.value + \"(\" + submission +\")\";\n }\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End code to preserve responses\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.version;\n console.log('MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([fb]);\n }\n } else {\n console.log('MathJax not detected');\n }\n return false;\n }\n\n}\n\nfunction isValid(el, charC) {\n //console.log(\"Input char: \", charC);\n if (charC == 46) {\n if (el.value.indexOf('.') === -1) {\n return true;\n } else if (el.value.indexOf('/') != -1) {\n var parts = el.value.split('/');\n if (parts[1].indexOf('.') === -1) {\n return true;\n }\n }\n else {\n return false;\n }\n } else if (charC == 47) {\n if (el.value.indexOf('/') === -1) {\n if ((el.value != \"\") && (el.value != \".\")) {\n return true;\n } else {\n return false;\n }\n } else {\n return false;\n }\n } else if (charC == 45) {\n var edex = el.value.indexOf('e');\n if (edex == -1) {\n edex = el.value.indexOf('E');\n }\n\n if (el.value == \"\") {\n return true;\n } else if (edex == (el.value.length - 1)) { // If just after e or E\n return true;\n } else {\n return false;\n }\n } else if (charC == 101) { // \"e\"\n if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n // Prev symbol must be digit or decimal point:\n if (el.value.slice(-1).search(/\\d/) >= 0) {\n return true;\n } else if (el.value.slice(-1).search(/\\./) >= 0) {\n return true;\n } else {\n return false;\n }\n } else {\n return false;\n }\n } else {\n if (charC > 31 && (charC < 48 || charC > 57))\n return false;\n }\n return true;\n}\n\nfunction numeric_keypress(evnt) {\n var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n\n if (charC == 13) {\n check_numeric(this, evnt);\n } else {\n return isValid(this, charC);\n }\n}\n\n\n\n\n\nfunction make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n\n\n\n //console.log(answer);\n\n\n outerqDiv.className = \"NumericQn\";\n aDiv.style.display = 'block';\n\n var lab = document.createElement(\"label\");\n lab.className = \"InpLabel\";\n lab.textContent = \"Type numeric answer here:\";\n aDiv.append(lab);\n\n var inp = document.createElement(\"input\");\n inp.type = \"text\";\n //inp.id=\"input-\"+id;\n inp.id = id + \"-0\";\n inp.className = \"Input-text\";\n inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n if (\"precision\" in qa) {\n inp.setAttribute('data-precision', qa.precision);\n }\n aDiv.append(inp);\n //console.log(inp);\n\n //inp.addEventListener(\"keypress\", check_numeric);\n //inp.addEventListener(\"keypress\", numeric_keypress);\n /*\n inp.addEventListener(\"keypress\", function(event) {\n return numeric_keypress(this, event);\n }\n );\n */\n //inp.onkeypress=\"return numeric_keypress(this, event)\";\n inp.onkeypress = numeric_keypress;\n inp.onpaste = event => false;\n\n inp.addEventListener(\"focus\", function (event) {\n this.value = \"\";\n return false;\n }\n );\n\n\n}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([mydiv]);\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([mydiv]);\n } else {\n console.log(\"MathJax not found\");\n }\n }\n }\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"KTvSZwtEYmCM\")) {\n show_questions(questionsKTvSZwtEYmCM, KTvSZwtEYmCM); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"KTvSZwtEYmCM\");\n // console.log(document.getElementById(\"KTvSZwtEYmCM\"));\n\n try_show();\n }\n " }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_quiz(\"#q_demo_seq3\")" ], "metadata": { "tags": [ "remove-input" ], "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:57.718511Z", "start_time": "2023-12-15T05:24:57.496818Z" } } }, { "cell_type": "markdown", "source": [ "#### CPU vs GPU\n", "\n", "
        \n", "\n", "![](assets/img.png)\n", "\n", "CPU-powered machine learning tasks with XGBoost can literally take hours to run. That’s because creating highly accurate, state-of-the-art prediction results involves the creation of thousands of decision trees and the testing of large numbers of parameter combinations. Graphics processing units, or GPUs, with their massively parallel architecture consisting of thousands of small efficient cores, can launch thousands of parallel threads simultaneously to supercharge compute-intensive tasks.\n", "\n" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# XGBOOST Features" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Algorithm Enhancements\n", "\n", "1. Tree [Pruning](https://www.displayr.com/machine-learning-pruning-decision-trees/) is a method employed in machine learning to decrease the size of regression trees. This is achieved by replacing nodes that do not contribute to enhancing classification on the leaves. The primary objective of pruning a regression tree is to prevent overfitting of the training data. The most effective approach for pruning is the Cost Complexity or Weakest Link Pruning, which utilizes mean square error, k-fold cross-validation, and learning rate internally. In the case of XGBoost, the algorithm generates nodes (or splits) up to a specified max_depth and initiates pruning from the end, progressing backward until the loss falls below a defined threshold. To decide whether to remove a split, XGBoost considers the cumulative loss by computing the total loss (-3 + 7 = +4) and retains both the split and subsequent node if the result is positive.\n", "\n", "2. Sparsity-Aware Split Discovery is a technique frequently employed when dealing with data that exhibits sparsity, characterized by a significant presence of missing or empty values. This sparsity may arise either inherently in the collected data or result from data engineering processes such as feature encoding. To account for sparsity patterns in the data, each tree is assigned a default direction. XGBoost addresses missing data by assigning them to the default direction and determining the optimal imputation value that minimizes the training loss. The optimization strategy involves selectively visiting only the missing values, resulting in a significant acceleration of the algorithm—up to 50 times faster than the naive method.\n", "\n", "\n", "### System Enhancements\n", "\n", "1. Parallelization is a crucial aspect of tree learning, where data needs to be organized in a sorted manner. To mitigate the expenses associated with sorting, the data is partitioned into compressed blocks, each containing a column with its corresponding feature values. XGBoost enhances efficiency by concurrently sorting each block using all the available cores/threads of the CPU. This optimization is particularly beneficial because a substantial number of nodes are regularly generated in the process of building a tree. In essence, XGBoost achieves parallelization by distributing the workload of sequentially generating trees across multiple cores or threads.\n", "2. Through cache-aware optimization, gradient statistics (both direction and value) for each split node are stored in an internal buffer specific to each thread. The accumulation of these statistics is done in a mini-batch manner, effectively minimizing the time overhead associated with immediate read/write operations and preventing cache misses. The optimization strategy is rooted in achieving cache awareness, and this is facilitated by selecting an optimal block size, typically around 2¹⁶. In summary, the cache-aware approach enhances efficiency by strategically managing and utilizing cache resources, resulting in improved performance for the algorithm.\n", "\n", "\n", "### Flexibility in XGBoost\n", "\n", "1. A customized objective function in machine learning serves the purpose of either maximizing or minimizing a specific criterion. In the context of machine learning, the goal is typically to minimize the objective function, which is a composite of the loss function and a regularization term. The objective function encapsulates the measure of how well the model performs, incorporating both the accuracy in predicting outcomes (loss function) and any regularization constraints imposed to prevent overfitting or enhance generalization. Customization of the objective function allows practitioners to tailor the optimization process according to the specific goals and characteristics of their machine learning task.\n", "\n", "
        \n", "\n", "![](assets/img_4.png)\n", "\n", " - Optimizing the loss function encourages predictive models whereas optimizing regularization leads to smaller variance and makes prediction stable. Different objective functions available in XGBoost are:\n", "\n", " * reg: linear for regression\n", " * reg: logistic, and binary: logistic for binary classification\n", " * multi: softmax, and multi: softprob for multiclass classification\n", " \n", " - Customized Evaluation Metric — This is a metric used to monitor the model’s accuracy on validation data.\n", " ● rmse — Root mean squared error (Regression)\n", " ● mae — Mean absolute error (Regression)\n", " ● error — Binary classification error (Classification)\n", " ● logloss — Negative log-likelihood (Classification)\n", " ● auc — Area under the curve (Classification)" ] }, { "cell_type": "markdown", "source": [ "
        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
        " ], "metadata": { "tags": [ "remove-input" ], "collapsed": false } }, { "cell_type": "code", "execution_count": 393, "outputs": [ { "data": { "text/plain": "", "text/html": "
        " }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": "", "application/javascript": "var element=document.getElementById(\"q_demo_seq4\");\n var questionsZmVnehYIEPox;\n try {\n questionsZmVnehYIEPox=JSON.parse(window.atob(element.innerHTML));\n } catch(err) {\n console.log(\"Fell into catch\");\n questionsZmVnehYIEPox = JSON.parse(element.innerHTML);\n }\n console.log(questionsZmVnehYIEPox);;\n // Make a random ID\nfunction makeid(length) {\n var result = [];\n var characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz';\n var charactersLength = characters.length;\n for (var i = 0; i < length; i++) {\n result.push(characters.charAt(Math.floor(Math.random() * charactersLength)));\n }\n return result.join('');\n}\n\n// Choose a random subset of an array. Can also be used to shuffle the array\nfunction getRandomSubarray(arr, size) {\n var shuffled = arr.slice(0), i = arr.length, temp, index;\n while (i--) {\n index = Math.floor((i + 1) * Math.random());\n temp = shuffled[index];\n shuffled[index] = shuffled[i];\n shuffled[i] = temp;\n }\n return shuffled.slice(0, size);\n}\n\nfunction printResponses(responsesContainer) {\n var responses=JSON.parse(responsesContainer.dataset.responses);\n var stringResponses='IMPORTANT!To preserve this answer sequence for submission, when you have finalized your answers:
        1. Copy the text in this cell below \"Answer String\"
        2. Double click on the cell directly below the Answer String, labeled \"Replace Me\"
        3. Select the whole \"Replace Me\" text
        4. Paste in your answer string and press shift-Enter.
        5. Save the notebook using the save icon or File->Save Notebook menu item



          6. Answer String:
          ';\n console.log(responses);\n responses.forEach((response, index) => {\n if (response) {\n console.log(index + ': ' + response);\n stringResponses+= index + ': ' + response +\"
          \";\n }\n });\n responsesContainer.innerHTML=stringResponses;\n}\nfunction check_mc() {\n var id = this.id.split('-')[0];\n //var response = this.id.split('-')[1];\n //console.log(response);\n //console.log(\"In check_mc(), id=\"+id);\n //console.log(event.srcElement.id) \n //console.log(event.srcElement.dataset.correct) \n //console.log(event.srcElement.dataset.feedback)\n\n var label = event.srcElement;\n //console.log(label, label.nodeName);\n var depth = 0;\n while ((label.nodeName != \"LABEL\") && (depth < 20)) {\n label = label.parentElement;\n console.log(depth, label);\n depth++;\n }\n\n\n\n var answers = label.parentElement.children;\n\n //console.log(answers);\n\n\n // Split behavior based on multiple choice vs many choice:\n var fb = document.getElementById(\"fb\" + id);\n\n\n\n\n if (fb.dataset.numcorrect == 1) {\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n //console.log(responsesContainer);\n var response = label.firstChild.innerText;\n if (label.querySelector(\".QuizCode\")){\n response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n }\n console.log(response);\n //console.log(document.getElementById(\"quizWrap\"+id));\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n console.log(responses);\n responses[qnum]= response;\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End code to preserve responses\n \n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n //console.log(child);\n child.className = \"MCButton\";\n }\n\n\n\n if (label.dataset.correct == \"true\") {\n // console.log(\"Correct action\");\n if (\"feedback\" in label.dataset) {\n fb.textContent = jaxify(label.dataset.feedback);\n } else {\n fb.textContent = \"Correct!\";\n }\n label.classList.add(\"correctButton\");\n\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n\n } else {\n if (\"feedback\" in label.dataset) {\n fb.textContent = jaxify(label.dataset.feedback);\n } else {\n fb.textContent = \"Incorrect -- try again.\";\n }\n //console.log(\"Error action\");\n label.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n }\n else {\n var reset = false;\n var feedback;\n if (label.dataset.correct == \"true\") {\n if (\"feedback\" in label.dataset) {\n feedback = jaxify(label.dataset.feedback);\n } else {\n feedback = \"Correct!\";\n }\n if (label.dataset.answered <= 0) {\n if (fb.dataset.answeredcorrect < 0) {\n fb.dataset.answeredcorrect = 1;\n reset = true;\n } else {\n fb.dataset.answeredcorrect++;\n }\n if (reset) {\n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n child.className = \"MCButton\";\n child.dataset.answered = 0;\n }\n }\n label.classList.add(\"correctButton\");\n label.dataset.answered = 1;\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n\n }\n } else {\n if (\"feedback\" in label.dataset) {\n feedback = jaxify(label.dataset.feedback);\n } else {\n feedback = \"Incorrect -- try again.\";\n }\n if (fb.dataset.answeredcorrect > 0) {\n fb.dataset.answeredcorrect = -1;\n reset = true;\n } else {\n fb.dataset.answeredcorrect--;\n }\n\n if (reset) {\n for (var i = 0; i < answers.length; i++) {\n var child = answers[i];\n child.className = \"MCButton\";\n child.dataset.answered = 0;\n }\n }\n label.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n //console.log(responsesContainer);\n var response = label.firstChild.innerText;\n if (label.querySelector(\".QuizCode\")){\n response+= label.querySelector(\".QuizCode\").firstChild.innerText;\n }\n console.log(response);\n //console.log(document.getElementById(\"quizWrap\"+id));\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n if (label.dataset.correct == \"true\") {\n if (typeof(responses[qnum]) == \"object\"){\n if (!responses[qnum].includes(response))\n responses[qnum].push(response);\n } else{\n responses[qnum]= [ response ];\n }\n } else {\n responses[qnum]= response;\n }\n console.log(responses);\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End save responses stuff\n\n\n\n var numcorrect = fb.dataset.numcorrect;\n var answeredcorrect = fb.dataset.answeredcorrect;\n if (answeredcorrect >= 0) {\n fb.textContent = feedback + \" [\" + answeredcorrect + \"/\" + numcorrect + \"]\";\n } else {\n fb.textContent = feedback + \" [\" + 0 + \"/\" + numcorrect + \"]\";\n }\n\n\n }\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.version;\n console.log('MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([fb]);\n }\n } else {\n console.log('MathJax not detected');\n }\n\n}\n\nfunction make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id) {\n var shuffled;\n if (shuffle_answers == \"True\") {\n //console.log(shuffle_answers+\" read as true\");\n shuffled = getRandomSubarray(qa.answers, qa.answers.length);\n } else {\n //console.log(shuffle_answers+\" read as false\");\n shuffled = qa.answers;\n }\n\n\n var num_correct = 0;\n\n\n\n shuffled.forEach((item, index, ans_array) => {\n //console.log(answer);\n\n // Make input element\n var inp = document.createElement(\"input\");\n inp.type = \"radio\";\n inp.id = \"quizo\" + id + index;\n inp.style = \"display:none;\";\n aDiv.append(inp);\n\n //Make label for input element\n var lab = document.createElement(\"label\");\n lab.className = \"MCButton\";\n lab.id = id + '-' + index;\n lab.onclick = check_mc;\n var aSpan = document.createElement('span');\n aSpan.classsName = \"\";\n //qDiv.id=\"quizQn\"+id+index;\n if (\"answer\" in item) {\n aSpan.innerHTML = jaxify(item.answer);\n //aSpan.innerHTML=item.answer;\n }\n lab.append(aSpan);\n\n // Create div for code inside question\n var codeSpan;\n if (\"code\" in item) {\n codeSpan = document.createElement('span');\n codeSpan.id = \"code\" + id + index;\n codeSpan.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeSpan.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = item.code;\n lab.append(codeSpan);\n //console.log(codeSpan);\n }\n\n //lab.textContent=item.answer;\n\n // Set the data attributes for the answer\n lab.setAttribute('data-correct', item.correct);\n if (item.correct) {\n num_correct++;\n }\n if (\"feedback\" in item) {\n lab.setAttribute('data-feedback', item.feedback);\n }\n lab.setAttribute('data-answered', 0);\n\n aDiv.append(lab);\n\n });\n\n if (num_correct > 1) {\n outerqDiv.className = \"ManyChoiceQn\";\n } else {\n outerqDiv.className = \"MultipleChoiceQn\";\n }\n\n return num_correct;\n\n}\nfunction check_numeric(ths, event) {\n\n if (event.keyCode === 13) {\n ths.blur();\n\n var id = ths.id.split('-')[0];\n\n var submission = ths.value;\n if (submission.indexOf('/') != -1) {\n var sub_parts = submission.split('/');\n //console.log(sub_parts);\n submission = sub_parts[0] / sub_parts[1];\n }\n //console.log(\"Reader entered\", submission);\n\n if (\"precision\" in ths.dataset) {\n var precision = ths.dataset.precision;\n // console.log(\"1:\", submission)\n submission = Math.round((1 * submission + Number.EPSILON) * 10 ** precision) / 10 ** precision;\n // console.log(\"Rounded to \", submission, \" precision=\", precision );\n }\n\n\n //console.log(\"In check_numeric(), id=\"+id);\n //console.log(event.srcElement.id) \n //console.log(event.srcElement.dataset.feedback)\n\n var fb = document.getElementById(\"fb\" + id);\n fb.style.display = \"none\";\n fb.textContent = \"Incorrect -- try again.\";\n\n var answers = JSON.parse(ths.dataset.answers);\n //console.log(answers);\n\n var defaultFB = \"\";\n var correct;\n var done = false;\n answers.every(answer => {\n //console.log(answer.type);\n\n correct = false;\n // if (answer.type==\"value\"){\n if ('value' in answer) {\n if (submission == answer.value) {\n if (\"feedback\" in answer) {\n fb.textContent = jaxify(answer.feedback);\n } else {\n fb.textContent = jaxify(\"Correct\");\n }\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n // } else if (answer.type==\"range\") {\n } else if ('range' in answer) {\n //console.log(answer.range);\n if ((submission >= answer.range[0]) && (submission < answer.range[1])) {\n fb.textContent = jaxify(answer.feedback);\n correct = answer.correct;\n //console.log(answer.correct);\n done = true;\n }\n } else if (answer.type == \"default\") {\n defaultFB = answer.feedback;\n }\n if (done) {\n return false; // Break out of loop if this has been marked correct\n } else {\n return true; // Keep looking for case that includes this as a correct answer\n }\n });\n\n if ((!done) && (defaultFB != \"\")) {\n fb.innerHTML = jaxify(defaultFB);\n //console.log(\"Default feedback\", defaultFB);\n }\n\n fb.style.display = \"block\";\n if (correct) {\n ths.className = \"Input-text\";\n ths.classList.add(\"correctButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"correct\");\n } else {\n ths.className = \"Input-text\";\n ths.classList.add(\"incorrectButton\");\n fb.className = \"Feedback\";\n fb.classList.add(\"incorrect\");\n }\n\n // What follows is for the saved responses stuff\n var outerContainer = fb.parentElement.parentElement;\n var responsesContainer = document.getElementById(\"responses\" + outerContainer.id);\n if (responsesContainer) {\n console.log(submission);\n var qnum = document.getElementById(\"quizWrap\"+id).dataset.qnum;\n //console.log(\"Question \" + qnum);\n //console.log(id, \", got numcorrect=\",fb.dataset.numcorrect);\n var responses=JSON.parse(responsesContainer.dataset.responses);\n console.log(responses);\n if (submission == ths.value){\n responses[qnum]= submission;\n } else {\n responses[qnum]= ths.value + \"(\" + submission +\")\";\n }\n responsesContainer.setAttribute('data-responses', JSON.stringify(responses));\n printResponses(responsesContainer);\n }\n // End code to preserve responses\n\n if (typeof MathJax != 'undefined') {\n var version = MathJax.version;\n console.log('MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([fb]);\n }\n } else {\n console.log('MathJax not detected');\n }\n return false;\n }\n\n}\n\nfunction isValid(el, charC) {\n //console.log(\"Input char: \", charC);\n if (charC == 46) {\n if (el.value.indexOf('.') === -1) {\n return true;\n } else if (el.value.indexOf('/') != -1) {\n var parts = el.value.split('/');\n if (parts[1].indexOf('.') === -1) {\n return true;\n }\n }\n else {\n return false;\n }\n } else if (charC == 47) {\n if (el.value.indexOf('/') === -1) {\n if ((el.value != \"\") && (el.value != \".\")) {\n return true;\n } else {\n return false;\n }\n } else {\n return false;\n }\n } else if (charC == 45) {\n var edex = el.value.indexOf('e');\n if (edex == -1) {\n edex = el.value.indexOf('E');\n }\n\n if (el.value == \"\") {\n return true;\n } else if (edex == (el.value.length - 1)) { // If just after e or E\n return true;\n } else {\n return false;\n }\n } else if (charC == 101) { // \"e\"\n if ((el.value.indexOf('e') === -1) && (el.value.indexOf('E') === -1) && (el.value.indexOf('/') == -1)) {\n // Prev symbol must be digit or decimal point:\n if (el.value.slice(-1).search(/\\d/) >= 0) {\n return true;\n } else if (el.value.slice(-1).search(/\\./) >= 0) {\n return true;\n } else {\n return false;\n }\n } else {\n return false;\n }\n } else {\n if (charC > 31 && (charC < 48 || charC > 57))\n return false;\n }\n return true;\n}\n\nfunction numeric_keypress(evnt) {\n var charC = (evnt.which) ? evnt.which : evnt.keyCode;\n\n if (charC == 13) {\n check_numeric(this, evnt);\n } else {\n return isValid(this, charC);\n }\n}\n\n\n\n\n\nfunction make_numeric(qa, outerqDiv, qDiv, aDiv, id) {\n\n\n\n //console.log(answer);\n\n\n outerqDiv.className = \"NumericQn\";\n aDiv.style.display = 'block';\n\n var lab = document.createElement(\"label\");\n lab.className = \"InpLabel\";\n lab.textContent = \"Type numeric answer here:\";\n aDiv.append(lab);\n\n var inp = document.createElement(\"input\");\n inp.type = \"text\";\n //inp.id=\"input-\"+id;\n inp.id = id + \"-0\";\n inp.className = \"Input-text\";\n inp.setAttribute('data-answers', JSON.stringify(qa.answers));\n if (\"precision\" in qa) {\n inp.setAttribute('data-precision', qa.precision);\n }\n aDiv.append(inp);\n //console.log(inp);\n\n //inp.addEventListener(\"keypress\", check_numeric);\n //inp.addEventListener(\"keypress\", numeric_keypress);\n /*\n inp.addEventListener(\"keypress\", function(event) {\n return numeric_keypress(this, event);\n }\n );\n */\n //inp.onkeypress=\"return numeric_keypress(this, event)\";\n inp.onkeypress = numeric_keypress;\n inp.onpaste = event => false;\n\n inp.addEventListener(\"focus\", function (event) {\n this.value = \"\";\n return false;\n }\n );\n\n\n}\nfunction jaxify(string) {\n var mystring = string;\n\n var count = 0;\n var loc = mystring.search(/([^\\\\]|^)(\\$)/);\n\n var count2 = 0;\n var loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n\n //console.log(loc);\n\n while ((loc >= 0) || (loc2 >= 0)) {\n\n /* Have to replace all the double $$ first with current implementation */\n if (loc2 >= 0) {\n if (count2 % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\[\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$\\$)/, \"$1\\\\]\");\n }\n count2++;\n } else {\n if (count % 2 == 0) {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\(\");\n } else {\n mystring = mystring.replace(/([^\\\\]|^)(\\$)/, \"$1\\\\)\");\n }\n count++;\n }\n loc = mystring.search(/([^\\\\]|^)(\\$)/);\n loc2 = mystring.search(/([^\\\\]|^)(\\$\\$)/);\n //console.log(mystring,\", loc:\",loc,\", loc2:\",loc2);\n }\n\n //console.log(mystring);\n return mystring;\n}\n\n\nfunction show_questions(json, mydiv) {\n console.log('show_questions');\n //var mydiv=document.getElementById(myid);\n var shuffle_questions = mydiv.dataset.shufflequestions;\n var num_questions = mydiv.dataset.numquestions;\n var shuffle_answers = mydiv.dataset.shuffleanswers;\n var max_width = mydiv.dataset.maxwidth;\n\n if (num_questions > json.length) {\n num_questions = json.length;\n }\n\n var questions;\n if ((num_questions < json.length) || (shuffle_questions == \"True\")) {\n //console.log(num_questions+\",\"+json.length);\n questions = getRandomSubarray(json, num_questions);\n } else {\n questions = json;\n }\n\n //console.log(\"SQ: \"+shuffle_questions+\", NQ: \" + num_questions + \", SA: \", shuffle_answers);\n\n // Iterate over questions\n questions.forEach((qa, index, array) => {\n //console.log(qa.question); \n\n var id = makeid(8);\n //console.log(id);\n\n\n // Create Div to contain question and answers\n var iDiv = document.createElement('div');\n //iDiv.id = 'quizWrap' + id + index;\n iDiv.id = 'quizWrap' + id;\n iDiv.className = 'Quiz';\n iDiv.setAttribute('data-qnum', index);\n iDiv.style.maxWidth =max_width+\"px\";\n mydiv.appendChild(iDiv);\n // iDiv.innerHTML=qa.question;\n \n var outerqDiv = document.createElement('div');\n outerqDiv.id = \"OuterquizQn\" + id + index;\n // Create div to contain question part\n var qDiv = document.createElement('div');\n qDiv.id = \"quizQn\" + id + index;\n \n if (qa.question) {\n iDiv.append(outerqDiv);\n\n //qDiv.textContent=qa.question;\n qDiv.innerHTML = jaxify(qa.question);\n outerqDiv.append(qDiv);\n }\n\n // Create div for code inside question\n var codeDiv;\n if (\"code\" in qa) {\n codeDiv = document.createElement('div');\n codeDiv.id = \"code\" + id + index;\n codeDiv.className = \"QuizCode\";\n var codePre = document.createElement('pre');\n codeDiv.append(codePre);\n var codeCode = document.createElement('code');\n codePre.append(codeCode);\n codeCode.innerHTML = qa.code;\n outerqDiv.append(codeDiv);\n //console.log(codeDiv);\n }\n\n\n // Create div to contain answer part\n var aDiv = document.createElement('div');\n aDiv.id = \"quizAns\" + id + index;\n aDiv.className = 'Answer';\n iDiv.append(aDiv);\n\n //console.log(qa.type);\n\n var num_correct;\n if ((qa.type == \"multiple_choice\") || (qa.type == \"many_choice\") ) {\n num_correct = make_mc(qa, shuffle_answers, outerqDiv, qDiv, aDiv, id);\n if (\"answer_cols\" in qa) {\n //aDiv.style.gridTemplateColumns = 'auto '.repeat(qa.answer_cols);\n aDiv.style.gridTemplateColumns = 'repeat(' + qa.answer_cols + ', 1fr)';\n }\n } else if (qa.type == \"numeric\") {\n //console.log(\"numeric\");\n make_numeric(qa, outerqDiv, qDiv, aDiv, id);\n }\n\n\n //Make div for feedback\n var fb = document.createElement(\"div\");\n fb.id = \"fb\" + id;\n //fb.style=\"font-size: 20px;text-align:center;\";\n fb.className = \"Feedback\";\n fb.setAttribute(\"data-answeredcorrect\", 0);\n fb.setAttribute(\"data-numcorrect\", num_correct);\n iDiv.append(fb);\n\n\n });\n var preserveResponses = mydiv.dataset.preserveresponses;\n console.log(preserveResponses);\n console.log(preserveResponses == \"true\");\n if (preserveResponses == \"true\") {\n console.log(preserveResponses);\n // Create Div to contain record of answers\n var iDiv = document.createElement('div');\n iDiv.id = 'responses' + mydiv.id;\n iDiv.className = 'JCResponses';\n // Create a place to store responses as an empty array\n iDiv.setAttribute('data-responses', '[]');\n\n // Dummy Text\n iDiv.innerHTML=\"Select your answers and then follow the directions that will appear here.\"\n //iDiv.className = 'Quiz';\n mydiv.appendChild(iDiv);\n }\n//console.log(\"At end of show_questions\");\n if (typeof MathJax != 'undefined') {\n console.log(\"MathJax version\", MathJax.version);\n var version = MathJax.version;\n setTimeout(function(){\n var version = MathJax.version;\n console.log('After sleep, MathJax version', version);\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([mydiv]);\n }\n }, 500);\nif (typeof version == 'undefined') {\n } else\n {\n if (version[0] == \"2\") {\n MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]);\n } else if (version[0] == \"3\") {\n MathJax.typeset([mydiv]);\n } else {\n console.log(\"MathJax not found\");\n }\n }\n }\n return false;\n}\n/* This is to handle asynchrony issues in loading Jupyter notebooks\n where the quiz has been previously run. The Javascript was generally\n being run before the div was added to the DOM. I tried to do this\n more elegantly using Mutation Observer, but I didn't get it to work.\n\n Someone more knowledgeable could make this better ;-) */\n\n function try_show() {\n if(document.getElementById(\"ZmVnehYIEPox\")) {\n show_questions(questionsZmVnehYIEPox, ZmVnehYIEPox); \n } else {\n setTimeout(try_show, 200);\n }\n };\n \n {\n // console.log(element);\n\n //console.log(\"ZmVnehYIEPox\");\n // console.log(document.getElementById(\"ZmVnehYIEPox\"));\n\n try_show();\n }\n " }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_quiz(\"#q_demo_seq4\")" ], "metadata": { "tags": [ "remove-input" ], "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:57.721992Z", "start_time": "2023-12-15T05:24:57.507277Z" } } }, { "cell_type": "markdown", "source": [ "# Implementation of Extreme Gradient Boosting " ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "The below code loads the dataset from the web. Here, we will use a classification problem predicting the click-through Rate. The purpose of click-through rate (CTR) prediction is to predict how likely a person will click on an advertisement or item" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "Import the required libraries" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 394, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import shap\n", "from sklearn.model_selection import train_test_split\n", "from xgboost import XGBClassifier\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n", "from sklearn.model_selection import RandomizedSearchCV\n", "from sklearn.model_selection import GridSearchCV\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "pd.set_option('display.max_columns', None)\n", "pd.set_option('display.max_rows', None)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:57.744675Z", "start_time": "2023-12-15T05:24:57.516013Z" } } }, { "cell_type": "markdown", "source": [ "Load the dataset from the web" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 395, "outputs": [], "source": [ "url=\"https://raw.githubusercontent.com/ataislucky/Data-Science/main/dataset/ad_ctr.csv\"\n", "ad_data = pd.read_csv(url)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:58.786673Z", "start_time": "2023-12-15T05:24:57.523703Z" } } }, { "cell_type": "code", "execution_count": 396, "outputs": [ { "data": { "text/plain": " Daily Time Spent on Site Age Area Income Daily Internet Usage \\\n0 62.26 32.0 69481.85 172.83 \n1 41.73 31.0 61840.26 207.17 \n2 44.40 30.0 57877.15 172.83 \n3 59.88 28.0 56180.93 207.17 \n4 49.21 30.0 54324.73 201.58 \n\n Ad Topic Line City Gender \\\n0 Decentralized real-time circuit Lisafort Male \n1 Optional full-range projection West Angelabury Male \n2 Total 5thgeneration standardization Reyesfurt Female \n3 Balanced empowering success New Michael Female \n4 Total 5thgeneration standardization West Richard Female \n\n Country Timestamp Clicked on Ad \n0 Svalbard & Jan Mayen Islands 2016-06-09 21:43:05 0 \n1 Singapore 2016-01-16 17:56:05 0 \n2 Guadeloupe 2016-06-29 10:50:45 0 \n3 Zambia 2016-06-21 14:32:32 0 \n4 Qatar 2016-07-21 10:54:35 1 ", "text/html": "
          \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
          Daily Time Spent on SiteAgeArea IncomeDaily Internet UsageAd Topic LineCityGenderCountryTimestampClicked on Ad
          062.2632.069481.85172.83Decentralized real-time circuitLisafortMaleSvalbard & Jan Mayen Islands2016-06-09 21:43:050
          141.7331.061840.26207.17Optional full-range projectionWest AngelaburyMaleSingapore2016-01-16 17:56:050
          244.4030.057877.15172.83Total 5thgeneration standardizationReyesfurtFemaleGuadeloupe2016-06-29 10:50:450
          359.8828.056180.93207.17Balanced empowering successNew MichaelFemaleZambia2016-06-21 14:32:320
          449.2130.054324.73201.58Total 5thgeneration standardizationWest RichardFemaleQatar2016-07-21 10:54:351
          \n
          " }, "execution_count": 396, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ad_data.head()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:58.793946Z", "start_time": "2023-12-15T05:24:58.790166Z" } } }, { "cell_type": "code", "execution_count": 397, "outputs": [ { "data": { "text/plain": "Daily Time Spent on Site 0\nAge 0\nArea Income 0\nDaily Internet Usage 0\nAd Topic Line 0\nCity 0\nGender 0\nCountry 0\nTimestamp 0\nClicked on Ad 0\ndtype: int64" }, "execution_count": 397, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check the missing value\n", "ad_data.isnull().sum()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:58.799321Z", "start_time": "2023-12-15T05:24:58.794466Z" } } }, { "cell_type": "code", "execution_count": 398, "outputs": [ { "data": { "text/plain": "Clicked on Ad\n0 0.5083\n1 0.4917\nName: proportion, dtype: float64" }, "execution_count": 398, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check the distribution of the target variable\n", "ad_data['Clicked on Ad'].value_counts(normalize=True)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:58.802720Z", "start_time": "2023-12-15T05:24:58.799006Z" } } }, { "cell_type": "markdown", "source": [ "Bar plot to visualize the target variable" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 399, "outputs": [ { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Clicked on Ad',data=ad_data, palette='hls')\n", "plt.show()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:58.936820Z", "start_time": "2023-12-15T05:24:58.803486Z" } } }, { "cell_type": "markdown", "source": [ "Time spent on the internet and clicks per hour plot" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 400, "outputs": [ { "data": { "text/plain": "Text(0.5, 1.0, 'Number of clicks by hour')" }, "execution_count": 400, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ad_data.groupby('Daily Time Spent on Site').agg({'Clicked on Ad':'sum'}).plot(figsize=(12,6))\n", "plt.ylabel('Number of clicks')\n", "plt.title('Number of clicks by hour')" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.180749Z", "start_time": "2023-12-15T05:24:58.925417Z" } } }, { "cell_type": "markdown", "source": [ "Boxplot to visualize the distribution of the feature and colour coded to understand the target variable" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 401, "outputs": [], "source": [ "def hue_boxplot(feature):\n", " \"\"\"\n", " Create a Plotly box plot with optional color-coding.\n", "\n", " Parameters:\n", " - feature\n", "\n", " Returns:\n", " - fig: A Plotly figure.\n", " \"\"\"\n", " fig = px.box(ad_data, \n", " x=feature, \n", " color=\"Clicked on Ad\", \n", " title=f\"{feature} on Daily Internet Usage\", \n", " color_discrete_map={'Yes':'blue',\n", " 'No':'red'})\n", " fig.update_traces(quartilemethod=\"exclusive\")\n", "\n", " return fig.show()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.185020Z", "start_time": "2023-12-15T05:24:59.181282Z" } } }, { "cell_type": "code", "execution_count": 402, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "alignmentgroup": "True", "hovertemplate": "Clicked on Ad=0
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0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ] }, "xaxis": { "gridcolor": "#283442", "linecolor": "#506784", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#283442", "automargin": true, "zerolinewidth": 2 }, "yaxis": { "gridcolor": "#283442", "linecolor": "#506784", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#283442", "automargin": true, "zerolinewidth": 2 }, "scene": { "xaxis": { "backgroundcolor": "rgb(17,17,17)", "gridcolor": "#506784", "linecolor": "#506784", "showbackground": true, "ticks": "", "zerolinecolor": "#C8D4E3", "gridwidth": 2 }, "yaxis": { "backgroundcolor": "rgb(17,17,17)", "gridcolor": "#506784", "linecolor": "#506784", "showbackground": true, "ticks": "", "zerolinecolor": "#C8D4E3", "gridwidth": 2 }, "zaxis": { "backgroundcolor": "rgb(17,17,17)", "gridcolor": "#506784", "linecolor": "#506784", "showbackground": true, "ticks": "", "zerolinecolor": "#C8D4E3", "gridwidth": 2 } }, "shapedefaults": { "line": { "color": "#f2f5fa" } }, "annotationdefaults": { "arrowcolor": "#f2f5fa", "arrowhead": 0, "arrowwidth": 1 }, "geo": { "bgcolor": "rgb(17,17,17)", "landcolor": "rgb(17,17,17)", "subunitcolor": "#506784", "showland": true, "showlakes": true, "lakecolor": "rgb(17,17,17)" }, "title": { "x": 0.05 }, "updatemenudefaults": { "bgcolor": "#506784", "borderwidth": 0 }, "sliderdefaults": { "bgcolor": "#C8D4E3", "borderwidth": 1, "bordercolor": "rgb(17,17,17)", "tickwidth": 0 }, "mapbox": { "style": "dark" } } }, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ], "title": { "text": "Area Income" } }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ] }, "legend": { "title": { "text": "Clicked on Ad" }, "tracegroupgap": 0 }, "title": { "text": "Area Income on Daily Internet Usage" }, "boxmode": "group" }, "config": { "plotlyServerURL": "https://plot.ly" } }, "text/html": "
          " }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hue_boxplot('Area Income')" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.298213Z", "start_time": "2023-12-15T05:24:59.251765Z" } } }, { "cell_type": "markdown", "source": [ "### Data Preparation and Analysis" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 405, "outputs": [ { "data": { "text/plain": "Gender\nFemale 0.5376\nMale 0.4624\nName: proportion, dtype: float64" }, "execution_count": 405, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ad_data['Gender'].value_counts(normalize=True)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.298794Z", "start_time": "2023-12-15T05:24:59.288427Z" } } }, { "cell_type": "markdown", "source": [ "Categorical columns need to be converted into numerical columns before feeding into the model." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 406, "outputs": [ { "data": { "text/plain": "Gender\n1 0.5376\n0 0.4624\nName: proportion, dtype: float64" }, "execution_count": 406, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gender_mapping = {'Male': 0, 'Female': 1}\n", "ad_data['Gender'] = ad_data['Gender'].map(gender_mapping)\n", "ad_data['Gender'].value_counts(normalize=True)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.299048Z", "start_time": "2023-12-15T05:24:59.292043Z" } } }, { "cell_type": "code", "execution_count": 407, "outputs": [ { "data": { "text/plain": "Country\n9 388\n44 330\n187 312\n149 224\n81 221\n14 190\n27 176\n2 174\n200 162\n20 162\nName: count, dtype: int64" }, "execution_count": 407, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use Label Encoding to convert the country column\n", "ad_data['Country'] = ad_data['Country'].astype('category').cat.codes\n", "ad_data['Country'].value_counts()[:10]" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.299725Z", "start_time": "2023-12-15T05:24:59.294995Z" } } }, { "cell_type": "code", "execution_count": 408, "outputs": [ { "data": { "text/plain": " Daily Time Spent on Site Age Area Income Daily Internet Usage \\\n0 62.26 32.0 69481.85 172.83 \n1 41.73 31.0 61840.26 207.17 \n\n Ad Topic Line City Gender Country \\\n0 Decentralized real-time circuit Lisafort 0 174 \n1 Optional full-range projection West Angelabury 0 166 \n\n Timestamp Clicked on Ad \n0 2016-06-09 21:43:05 0 \n1 2016-01-16 17:56:05 0 ", "text/html": "
          \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
          Daily Time Spent on SiteAgeArea IncomeDaily Internet UsageAd Topic LineCityGenderCountryTimestampClicked on Ad
          062.2632.069481.85172.83Decentralized real-time circuitLisafort01742016-06-09 21:43:050
          141.7331.061840.26207.17Optional full-range projectionWest Angelabury01662016-01-16 17:56:050
          \n
          " }, "execution_count": 408, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ad_data.head(2)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.313903Z", "start_time": "2023-12-15T05:24:59.304420Z" } } }, { "cell_type": "markdown", "source": [ "Dropping few unnecessary columns before model training" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 409, "outputs": [], "source": [ "ad_data.drop(['Ad Topic Line', 'City', 'Timestamp'], axis=1, inplace=True)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.314025Z", "start_time": "2023-12-15T05:24:59.310322Z" } } }, { "cell_type": "code", "execution_count": 410, "outputs": [ { "data": { "text/plain": "((10000, 6), (10000,))" }, "execution_count": 410, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#creating two dataset X and y : features dataset and target dataset\n", "X = ad_data.loc[:, ad_data.columns != 'Clicked on Ad']\n", "y = ad_data['Clicked on Ad']\n", "X.shape, y.shape" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.323738Z", "start_time": "2023-12-15T05:24:59.313800Z" } } }, { "cell_type": "markdown", "source": [ "Randomly split training set into train and test subsets" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 411, "outputs": [ { "data": { "text/plain": "((8000, 6), (2000, 6), (8000,), (2000,))" }, "execution_count": 411, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#split the dataset into train test 80:20 portion\n", "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=45)\n", "X_train.shape,X_test.shape,y_train.shape,y_test.shape" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.353776Z", "start_time": "2023-12-15T05:24:59.319186Z" } } }, { "cell_type": "markdown", "source": [ "### Model Training Build XGBoost Model and Make Predictions" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "It is imperative to divide our dataset into two distinct sets: the training set, which is used to train our model, and the testing set, which serves to evaluate how effectively our model fits the dataset.\n", "\n", "Once the split is done we move to train the base model, here we are using the default parameter for training the model to show its effectiveness." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 412, "outputs": [ { "data": { "text/plain": "XGBClassifier(base_score=None, booster=None, callbacks=None,\n colsample_bylevel=None, colsample_bynode=None,\n colsample_bytree=None, device=None, early_stopping_rounds=None,\n enable_categorical=False, eval_metric=None, feature_types=None,\n gamma=None, grow_policy=None, importance_type=None,\n interaction_constraints=None, learning_rate=None, max_bin=None,\n max_cat_threshold=None, max_cat_to_onehot=None,\n max_delta_step=None, max_depth=None, max_leaves=None,\n min_child_weight=None, missing=nan, monotone_constraints=None,\n multi_strategy=None, n_estimators=None, n_jobs=None,\n num_parallel_tree=None, random_state=None, ...)", "text/html": "
          XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=None, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=None, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=None, n_jobs=None,\n              num_parallel_tree=None, random_state=None, ...)
          In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
          On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
          " }, "execution_count": 412, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create and train the base XGBoost model with the default parameters\n", "model = XGBClassifier()\n", "model.fit(X_train, y_train)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.485023Z", "start_time": "2023-12-15T05:24:59.324028Z" } } }, { "cell_type": "markdown", "source": [ "Once trained we will use the model to predict the test data set and evaluate its performance on the test data set." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 413, "outputs": [], "source": [ "# Make predictions on the train dataset iteself\n", "y_pred_train = model.predict(X_train)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.494845Z", "start_time": "2023-12-15T05:24:59.481467Z" } } }, { "cell_type": "markdown", "source": [ "The below code evaluates the model performance." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 414, "outputs": [], "source": [ "#function to create the model evaluation metric\n", "def model_eval(y_actual, y_predicted):\n", " \n", " \"\"\"pass the actual and predicted data\"\"\"\n", " \n", " print('Accuracy:',accuracy_score(y_actual, y_predicted))\n", " print(classification_report(y_actual, y_predicted))\n", " cm = confusion_matrix(y_actual, y_predicted)\n", "\n", " # Display the confusion matrix as a heatmap\n", " plt.figure(figsize=(6, 6))\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False, square=True, linewidths=0.5)\n", " plt.xlabel('Predicted')\n", " plt.ylabel('True')\n", " plt.title('Confusion Matrix')\n", " return plt.show() " ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.495010Z", "start_time": "2023-12-15T05:24:59.489378Z" } } }, { "cell_type": "code", "execution_count": 415, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.947\n", " precision recall f1-score support\n", "\n", " 0 0.94 0.96 0.95 4065\n", " 1 0.95 0.94 0.95 3935\n", "\n", " accuracy 0.95 8000\n", " macro avg 0.95 0.95 0.95 8000\n", "weighted avg 0.95 0.95 0.95 8000\n" ] }, { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model_eval(y_train, y_pred_train)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.563497Z", "start_time": "2023-12-15T05:24:59.492379Z" } } }, { "cell_type": "code", "execution_count": 416, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.8345\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.86 0.84 1018\n", " 1 0.85 0.81 0.83 982\n", "\n", " accuracy 0.83 2000\n", " macro avg 0.84 0.83 0.83 2000\n", "weighted avg 0.83 0.83 0.83 2000\n" ] }, { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make predictions in the test\n", "y_pred_test = model.predict(X_test)\n", "model_eval(y_test, y_pred_test)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.629542Z", "start_time": "2023-12-15T05:24:59.563432Z" } } }, { "cell_type": "markdown", "source": [ "### SHAP Explainer" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "SHAP (SHapley Additive exPlanations) is a game theoretic approach to understand each player's contribution to the final outcome. This in turn explains the output of any ML model. ML models, especially ensembles, are considered black box models as they are difficult to interpret. It is harder to determine which are the important predictors for the model." ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "The code below, creates an explainer object by providing a XGBoost classification model, then calculates SHAP value using a testing set." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 417, "outputs": [], "source": [ "#create the explainer to plot the shap plots \n", "explainer = shap.Explainer(model)\n", "shap_values = explainer.shap_values(X_test)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:24:59.851462Z", "start_time": "2023-12-15T05:24:59.629193Z" } } }, { "cell_type": "code", "execution_count": 418, "outputs": [ { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#shap summary plot\n", "shap.summary_plot(shap_values, X_test)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:00.011075Z", "start_time": "2023-12-15T05:24:59.851831Z" } } }, { "cell_type": "markdown", "source": [ "* The Y-axis represents the feature names arranged in descending order of importance, with the most important features at the top and the least important ones at the bottom\n", "* The X-axis denotes the SHAP value, which serves as a measure of the extent of change in log odds\n", "* If we examine the \"Age\" feature and observe a notably positive value, it indicates that Age exerts a substantial positive influence on the output. In other words, when a person's age is higher, there is a heightened probability that the individual will be more inclined to click on the advertisement. Next, If we look at the feature “Daily Internet usage,\" we notice that it is mostly high with a negative SHAP value. It means high internet usage tends to negatively affect the output." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 419, "outputs": [ { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot summary_plot as barplot\n", "shap.summary_plot(shap_values, X_test, plot_type='bar')" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:00.079Z", "start_time": "2023-12-15T05:25:00.011385Z" } } }, { "cell_type": "markdown", "source": [ "The summary plot shows the feature importance of each feature in the model. The results show that “Age,” “Country,” and “Daily Internet Usage” play major roles as predictors." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 420, "outputs": [ { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot dependence_plot\n", "shap.dependence_plot(\"Age\", shap_values, X_test)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:00.190382Z", "start_time": "2023-12-15T05:25:00.078713Z" } } }, { "cell_type": "markdown", "source": [ "* Each dot is a single prediction from the dataset, the x-axis is the value of age,\n", "* The y-axis is the SHAP value for that feature, which represents how much knowing that feature’s value changes the output of the model for that sample’s prediction. For this model the units are log-odds of clicking the ad.\n", "* For example, a 60 year old with high internet usage is more likely to click on the ad. \n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 421, "outputs": [ { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot decision_plot\n", "expected_value = explainer.expected_value\n", "shap.decision_plot(expected_value, shap_values, X_test)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:01.237364Z", "start_time": "2023-12-15T05:25:00.190773Z" } } }, { "cell_type": "markdown", "source": [ "Every line depicted on the decision plot illustrates the level of influence of individual features on a specific model prediction, thereby elucidating which feature values had the most impact on that prediction." ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "### Hyperparameter tuning and finding the best parameter" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "Hyperparameter tuning in XGBoost is a crucial step to optimize the performance of your model. Here are the key steps and considerations for XGBoost hyperparameter tuning:\n", "\n", "To find the best parameter we will use GridSearchCV and Randomized search CV. The below code is used to train the model and find the best parameters." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 422, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-logloss:0.66228\n", "[1]\tvalidation_0-logloss:0.63665\n", "[2]\tvalidation_0-logloss:0.61491\n", "[3]\tvalidation_0-logloss:0.59086\n", "[4]\tvalidation_0-logloss:0.56276\n", "[5]\tvalidation_0-logloss:0.54601\n", "[6]\tvalidation_0-logloss:0.52561\n", "[7]\tvalidation_0-logloss:0.51327\n", "[8]\tvalidation_0-logloss:0.50077\n", "[9]\tvalidation_0-logloss:0.49192\n", "[10]\tvalidation_0-logloss:0.48133\n", "[11]\tvalidation_0-logloss:0.47366\n", "[12]\tvalidation_0-logloss:0.46420\n", "[13]\tvalidation_0-logloss:0.45403\n", "[14]\tvalidation_0-logloss:0.44896\n", "[15]\tvalidation_0-logloss:0.44449\n", "[16]\tvalidation_0-logloss:0.43877\n", "[17]\tvalidation_0-logloss:0.43207\n", "[18]\tvalidation_0-logloss:0.42665\n", "[19]\tvalidation_0-logloss:0.42255\n", "[20]\tvalidation_0-logloss:0.41929\n", "[21]\tvalidation_0-logloss:0.41576\n", "[22]\tvalidation_0-logloss:0.41379\n", "[23]\tvalidation_0-logloss:0.41015\n", "[24]\tvalidation_0-logloss:0.40822\n", "[25]\tvalidation_0-logloss:0.40353\n", "[26]\tvalidation_0-logloss:0.39922\n", "[27]\tvalidation_0-logloss:0.39785\n", "[28]\tvalidation_0-logloss:0.39542\n", "[29]\tvalidation_0-logloss:0.39273\n", "[30]\tvalidation_0-logloss:0.39039\n", "[31]\tvalidation_0-logloss:0.38907\n", "[32]\tvalidation_0-logloss:0.38555\n", "[33]\tvalidation_0-logloss:0.38367\n", "[34]\tvalidation_0-logloss:0.38270\n", "[35]\tvalidation_0-logloss:0.38047\n", "[36]\tvalidation_0-logloss:0.37928\n", "[37]\tvalidation_0-logloss:0.37675\n", "[38]\tvalidation_0-logloss:0.37508\n", "[39]\tvalidation_0-logloss:0.37276\n", "[40]\tvalidation_0-logloss:0.37239\n", "[41]\tvalidation_0-logloss:0.37183\n", "[42]\tvalidation_0-logloss:0.37115\n", "[43]\tvalidation_0-logloss:0.36915\n", "[44]\tvalidation_0-logloss:0.36758\n", "[45]\tvalidation_0-logloss:0.36609\n", "[46]\tvalidation_0-logloss:0.36448\n", "[47]\tvalidation_0-logloss:0.36339\n", "[48]\tvalidation_0-logloss:0.36286\n", "[49]\tvalidation_0-logloss:0.36222\n", "[50]\tvalidation_0-logloss:0.36218\n", "[51]\tvalidation_0-logloss:0.36095\n", "[52]\tvalidation_0-logloss:0.36083\n", "[53]\tvalidation_0-logloss:0.35991\n", "[54]\tvalidation_0-logloss:0.35934\n", "[55]\tvalidation_0-logloss:0.35821\n", "[56]\tvalidation_0-logloss:0.35775\n", "[57]\tvalidation_0-logloss:0.35718\n", "[58]\tvalidation_0-logloss:0.35699\n", "[59]\tvalidation_0-logloss:0.35684\n", "[60]\tvalidation_0-logloss:0.35690\n", "[61]\tvalidation_0-logloss:0.35702\n", "[62]\tvalidation_0-logloss:0.35679\n", "[63]\tvalidation_0-logloss:0.35641\n", "[64]\tvalidation_0-logloss:0.35563\n", "[65]\tvalidation_0-logloss:0.35507\n", "[66]\tvalidation_0-logloss:0.35539\n", "[67]\tvalidation_0-logloss:0.35529\n", "[68]\tvalidation_0-logloss:0.35504\n", "[69]\tvalidation_0-logloss:0.35501\n", "[70]\tvalidation_0-logloss:0.35501\n", "[71]\tvalidation_0-logloss:0.35479\n", "[72]\tvalidation_0-logloss:0.35401\n", "[73]\tvalidation_0-logloss:0.35372\n", "[74]\tvalidation_0-logloss:0.35322\n", "[75]\tvalidation_0-logloss:0.35336\n", "[76]\tvalidation_0-logloss:0.35309\n", "[77]\tvalidation_0-logloss:0.35285\n", "[78]\tvalidation_0-logloss:0.35290\n", "[79]\tvalidation_0-logloss:0.35332\n", "[80]\tvalidation_0-logloss:0.35326\n", "[81]\tvalidation_0-logloss:0.35326\n" ] }, { "data": { "text/plain": "XGBClassifier(base_score=None, booster=None, callbacks=None,\n colsample_bylevel=None, colsample_bynode=None,\n colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n enable_categorical=False, eval_metric=None, feature_types=None,\n gamma=None, grow_policy=None, importance_type=None,\n interaction_constraints=None, learning_rate=0.1, max_bin=None,\n max_cat_threshold=None, max_cat_to_onehot=None,\n max_delta_step=None, max_depth=12, max_leaves=None,\n min_child_weight=5, missing=nan, monotone_constraints=None,\n multi_strategy=None, n_estimators=100, n_jobs=None,\n num_parallel_tree=None, random_state=None, ...)", "text/html": "
          XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=12, max_leaves=None,\n              min_child_weight=5, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=100, n_jobs=None,\n              num_parallel_tree=None, random_state=None, ...)
          In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
          On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
          " }, "execution_count": 422, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Initialise model using best parameters\n", "model = XGBClassifier(objective=\"binary:logistic\",subsample=1,\n", "colsample_bytree=0.5,\n", "min_child_weight=5,\n", "max_depth=12,\n", "learning_rate=0.1,\n", "n_estimators=100)\n", "#Fit the model but stop early if there has been no reduction in error after 10 epochs.\n", "model.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_test, y_test)])" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:01.537401Z", "start_time": "2023-12-15T05:25:01.237276Z" } } }, { "cell_type": "markdown", "source": [ "Use the model to predict the target variable on the unseen dat" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 423, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.918875\n", " precision recall f1-score support\n", "\n", " 0 0.92 0.92 0.92 4065\n", " 1 0.92 0.92 0.92 3935\n", "\n", " accuracy 0.92 8000\n", " macro avg 0.92 0.92 0.92 8000\n", "weighted avg 0.92 0.92 0.92 8000\n" ] }, { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_predictions = model.predict(X_train)\n", "model_eval(y_train, train_predictions)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:01.622745Z", "start_time": "2023-12-15T05:25:01.540259Z" } } }, { "cell_type": "code", "execution_count": 424, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.842\n", " precision recall f1-score support\n", "\n", " 0 0.83 0.86 0.85 1018\n", " 1 0.85 0.82 0.84 982\n", "\n", " accuracy 0.84 2000\n", " macro avg 0.84 0.84 0.84 2000\n", "weighted avg 0.84 0.84 0.84 2000\n" ] }, { "data": { "text/plain": "
          ", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "predictions = model.predict(X_test)\n", "model_eval(y_test, predictions)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:01.698304Z", "start_time": "2023-12-15T05:25:01.624753Z" } } }, { "cell_type": "markdown", "source": [ "Next, we will again pass the parameters and this time we will also add the regularization parameter." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 425, "outputs": [], "source": [ "#here along with the other parameter passing the regularization to balance the bias and variance\n", "params = {'max_depth': [3, 6, 10, 15],\n", " 'learning_rate': [0.01, 0.1, 0.2, 0.3, 0.4],\n", " 'subsample': np.arange(0.5, 1.0, 0.1),\n", " 'colsample_bytree': np.arange(0.5, 1.0, 0.1),\n", " 'colsample_bylevel': np.arange(0.5, 1.0, 0.1),\n", " 'n_estimators': [100, 250, 500, 750],\n", " 'reg_alpha' : [0.1,0.001,.00001],\n", " 'reg_lambda': [0.1,0.001,.00001]\n", " \n", " \n", " }" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:01.698460Z", "start_time": "2023-12-15T05:25:01.694292Z" } } }, { "cell_type": "markdown", "source": [ "Instantiate the model, with 100 estimators." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 426, "outputs": [], "source": [ "xgbclf = XGBClassifier(n_estimators=100, n_jobs=-1)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:01.700990Z", "start_time": "2023-12-15T05:25:01.698168Z" } } }, { "cell_type": "markdown", "source": [ "We use RandomizedCV to find the best parameter" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 427, "outputs": [], "source": [ "#we use randomized cv to obtaine the best params\n", "clf = RandomizedSearchCV(estimator=xgbclf,\n", " param_distributions=params,\n", " scoring='accuracy',\n", " n_iter=25,\n", " n_jobs=4,\n", " verbose=1)\n" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:01.703991Z", "start_time": "2023-12-15T05:25:01.701122Z" } } }, { "cell_type": "markdown", "source": [ "Fit the model, find the best parameter and use it to predict the target variable." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 428, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n" ] }, { "data": { "text/plain": "RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n callbacks=None,\n colsample_bylevel=None,\n colsample_bynode=None,\n colsample_bytree=None, device=None,\n early_stopping_rounds=None,\n enable_categorical=False,\n eval_metric=None, feature_types=None,\n gamma=None, grow_policy=None,\n importance_type=None,\n interaction_constraints=None,\n learning_rate=None...\n n_iter=25, n_jobs=4,\n param_distributions={'colsample_bylevel': array([0.5, 0.6, 0.7, 0.8, 0.9]),\n 'colsample_bytree': array([0.5, 0.6, 0.7, 0.8, 0.9]),\n 'learning_rate': [0.01, 0.1, 0.2, 0.3,\n 0.4],\n 'max_depth': [3, 6, 10, 15],\n 'n_estimators': [100, 250, 500, 750],\n 'reg_alpha': [0.1, 0.001, 1e-05],\n 'reg_lambda': [0.1, 0.001, 1e-05],\n 'subsample': array([0.5, 0.6, 0.7, 0.8, 0.9])},\n scoring='accuracy', verbose=1)", "text/html": "
          RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n                                           callbacks=None,\n                                           colsample_bylevel=None,\n                                           colsample_bynode=None,\n                                           colsample_bytree=None, device=None,\n                                           early_stopping_rounds=None,\n                                           enable_categorical=False,\n                                           eval_metric=None, feature_types=None,\n                                           gamma=None, grow_policy=None,\n                                           importance_type=None,\n                                           interaction_constraints=None,\n                                           learning_rate=None...\n                   n_iter=25, n_jobs=4,\n                   param_distributions={'colsample_bylevel': array([0.5, 0.6, 0.7, 0.8, 0.9]),\n                                        'colsample_bytree': array([0.5, 0.6, 0.7, 0.8, 0.9]),\n                                        'learning_rate': [0.01, 0.1, 0.2, 0.3,\n                                                          0.4],\n                                        'max_depth': [3, 6, 10, 15],\n                                        'n_estimators': [100, 250, 500, 750],\n                                        'reg_alpha': [0.1, 0.001, 1e-05],\n                                        'reg_lambda': [0.1, 0.001, 1e-05],\n                                        'subsample': array([0.5, 0.6, 0.7, 0.8, 0.9])},\n                   scoring='accuracy', verbose=1)
          In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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          " }, "execution_count": 428, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.fit(X_train,y_train)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:09.076399Z", "start_time": "2023-12-15T05:25:01.704781Z" } } }, { "cell_type": "code", "execution_count": 429, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best hyperparameter combination: {'subsample': 0.5, 'reg_lambda': 0.1, 'reg_alpha': 1e-05, 'n_estimators': 500, 'max_depth': 3, 'learning_rate': 0.2, 'colsample_bytree': 0.6, 'colsample_bylevel': 0.7999999999999999}\n" ] } ], "source": [ "print(\"Best hyperparameter combination: \", clf.best_params_)" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:09.080139Z", "start_time": "2023-12-15T05:25:09.076291Z" } } }, { "cell_type": "code", "execution_count": 429, "outputs": [], "source": [], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2023-12-15T05:25:09.081706Z", "start_time": "2023-12-15T05:25:09.079482Z" } } } ], "metadata": { "kernelspec": { "name": "python3", "language": "python", "display_name": "Python 3 (ipykernel)" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 4 }