From b01d1a67069ebecf5d43b180d293b0b9045d5910 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Tue, 3 Sep 2024 05:11:10 +0200 Subject: [PATCH] added p1 --- doc/LectureNotes/Project1.ipynb | 157 +++++++++--------- .../_build/.doctrees/environment.pickle | Bin 278560 -> 278749 bytes .../_build/.doctrees/intro.doctree | Bin 52923 -> 52923 bytes .../_build/.doctrees/project1.doctree | Bin 118169 -> 117734 bytes .../_build/html/_sources/project1.ipynb | 157 +++++++++--------- doc/LectureNotes/_build/html/genindex.html | 2 +- doc/LectureNotes/_build/html/intro.html | 2 +- doc/LectureNotes/_build/html/objects.inv | Bin 986 -> 990 bytes doc/LectureNotes/_build/html/project1.html | 43 +++-- doc/LectureNotes/_build/html/search.html | 2 +- doc/LectureNotes/_build/html/searchindex.js | 2 +- .../_build/jupyter_execute/project1.ipynb | 157 +++++++++--------- .../_build/jupyter_execute/project1.py | 45 ++--- doc/LectureNotes/_toc.yml | 2 +- 14 files changed, 298 insertions(+), 271 deletions(-) diff --git a/doc/LectureNotes/Project1.ipynb b/doc/LectureNotes/Project1.ipynb index 71e7871a5..816cc7c52 100644 --- a/doc/LectureNotes/Project1.ipynb +++ b/doc/LectureNotes/Project1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f29d1e5a", + "id": "6c8c59f8", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "60f7a134", + "id": "24af4cf5", "metadata": { "editable": true }, @@ -27,34 +27,40 @@ }, { "cell_type": "markdown", - "id": "43b931d0", + "id": "2971d68f", "metadata": { "editable": true }, "source": [ "## Preamble: Note on writing reports, using reference material, AI and other tools\n", "\n", - "We want you to answer the three different projects by handing reports written like a standard scientific/technical report.\n", - "The link at gives some guidance. See also the grading suggestion at .\n", + "We want you to answer the three different projects by handing in\n", + "reports written like a standard scientific/technical report. The link\n", + "at\n", + "\n", + "gives some guidance. See also the grading suggestion at\n", + ".\n", "\n", - "When using codes from different sources that you have not developed yourself,\n", - "you should refer to these in the bibliography of your report, indicating wherefrom you\n", + "Furthermore, at\n", + "\n", + "you can find examples of previous reports. How to write reports will\n", + "also be discussed during the various lab sessions. Please do ask us if you are in doubt.\n", + "\n", + "When using codes and material from other sources, you should refer to these in the bibliography of your report, indicating wherefrom you for example\n", "got the code, whether this is from the lecture notes, softwares like\n", "Scikit-Learn, TensorFlow, PyTorch or other sources. These should\n", "always be cited correctly. How to cite some of the libraries is often\n", "indicated from their corresponding GitHub sites or websites, see for example how to cite Scikit-Learn at . \n", "\n", "We enocurage you to use tools like\n", - "[ChatGPT](https://openai.com/chatgpt/) in writing the report. If you use for example ChatGPT,\n", + "[ChatGPT](https://openai.com/chatgpt/) or similar in writing the report. If you use for example ChatGPT,\n", "please do cite it properly and include (if possible) your questions and answers as an addition to the report. This can\n", - "be uplodaed to for example your website, GitHub/GitLab or similar as supplemental material.\n", - "\n", - "On scaling, we recommend reading the following section from the scikit-learn software description, see " + "be uploaded to for example your website, GitHub/GitLab or similar as supplemental material." ] }, { "cell_type": "markdown", - "id": "131f84dc", + "id": "eba3be6a", "metadata": { "editable": true }, @@ -64,14 +70,7 @@ "The main aim of this project is to study in more detail various\n", "regression methods, including the Ordinary Least Squares (OLS) method.\n", "In addition to the scientific part, in this course we want also to\n", - "give you an experience in writing scientific reports. The format for\n", - "the delivery of your answers is namely that of a scientific report. At\n", - "for example\n", - "\n", - "we detail how to write a report. Furthermore, at\n", - "\n", - "you can find examples of previous reports. How to write reports will\n", - "also be discussed during the various lab sessions.\n", + "give you an experience in writing scientific reports.\n", "\n", "**A small recommendation when developing the codes here**. Instead of\n", "jumping on to the two-dimensional function described below, we\n", @@ -90,7 +89,7 @@ }, { "cell_type": "markdown", - "id": "335be2ea", + "id": "50da25e5", "metadata": { "editable": true }, @@ -112,7 +111,7 @@ }, { "cell_type": "markdown", - "id": "a77e527b", + "id": "7757fb0c", "metadata": { "editable": true }, @@ -127,7 +126,7 @@ }, { "cell_type": "markdown", - "id": "071b756f", + "id": "83fbdb79", "metadata": { "editable": true }, @@ -160,7 +159,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "eae160df", + "id": "39b900ad", "metadata": { "collapsed": false, "editable": true @@ -212,7 +211,17 @@ }, { "cell_type": "markdown", - "id": "c2cfaf54", + "id": "ef99fea4", + "metadata": { + "editable": true + }, + "source": [ + "If you wish to compare your results with other on the Franke function or other popular functions tested with linear regression, see the list in Figure 1 of the article by Cook et al at ." + ] + }, + { + "cell_type": "markdown", + "id": "b1a7f51d", "metadata": { "editable": true }, @@ -234,7 +243,7 @@ }, { "cell_type": "markdown", - "id": "a7cd4d8f", + "id": "d4bd58e3", "metadata": { "editable": true }, @@ -247,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "c0bbdb51", + "id": "32f767b4", "metadata": { "editable": true }, @@ -259,7 +268,7 @@ }, { "cell_type": "markdown", - "id": "ddee9b4b", + "id": "bae0d9d6", "metadata": { "editable": true }, @@ -271,7 +280,7 @@ }, { "cell_type": "markdown", - "id": "6f220536", + "id": "8f0b2ec9", "metadata": { "editable": true }, @@ -281,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "feb5938f", + "id": "8a455920", "metadata": { "editable": true }, @@ -293,7 +302,7 @@ }, { "cell_type": "markdown", - "id": "8ca0b371", + "id": "3f7ffe3e", "metadata": { "editable": true }, @@ -317,12 +326,14 @@ "approximately $2/3$ to $4/5$ of the data as training data.\n", "\n", "You can easily reuse the solutions to your exercises from week 35 and week 36.\n", - "See also the lecture slides from week 35 and week 36." + "See also the lecture slides from week 35 and week 36.\n", + "\n", + "On scaling, we recommend reading the following section from the scikit-learn software description, see ." ] }, { "cell_type": "markdown", - "id": "17494425", + "id": "cf4d70a4", "metadata": { "editable": true }, @@ -340,7 +351,7 @@ }, { "cell_type": "markdown", - "id": "79411fbf", + "id": "8cc60702", "metadata": { "editable": true }, @@ -357,7 +368,7 @@ }, { "cell_type": "markdown", - "id": "0de10c86", + "id": "bd9d1dd3", "metadata": { "editable": true }, @@ -373,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "b284b2d9", + "id": "03fab7b5", "metadata": { "editable": true }, @@ -385,7 +396,7 @@ }, { "cell_type": "markdown", - "id": "3a862737", + "id": "009b7fb9", "metadata": { "editable": true }, @@ -396,7 +407,7 @@ }, { "cell_type": "markdown", - "id": "8ca87494", + "id": "5bf0a0d5", "metadata": { "editable": true }, @@ -408,7 +419,7 @@ }, { "cell_type": "markdown", - "id": "a253c39b", + "id": "52c48acb", "metadata": { "editable": true }, @@ -420,7 +431,7 @@ }, { "cell_type": "markdown", - "id": "9c38fb19", + "id": "3158357a", "metadata": { "editable": true }, @@ -432,7 +443,7 @@ }, { "cell_type": "markdown", - "id": "8d59d872", + "id": "021253bc", "metadata": { "editable": true }, @@ -443,7 +454,7 @@ }, { "cell_type": "markdown", - "id": "0efd8089", + "id": "9e89d5fe", "metadata": { "editable": true }, @@ -455,7 +466,7 @@ }, { "cell_type": "markdown", - "id": "1a8121b6", + "id": "5f79916c", "metadata": { "editable": true }, @@ -468,7 +479,7 @@ }, { "cell_type": "markdown", - "id": "544da2b4", + "id": "a6e62eff", "metadata": { "editable": true }, @@ -480,7 +491,7 @@ }, { "cell_type": "markdown", - "id": "73fd411a", + "id": "7e833f14", "metadata": { "editable": true }, @@ -490,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "b7160507", + "id": "14ef5a97", "metadata": { "editable": true }, @@ -502,7 +513,7 @@ }, { "cell_type": "markdown", - "id": "b18ae3c3", + "id": "a9443b1d", "metadata": { "editable": true }, @@ -513,7 +524,7 @@ }, { "cell_type": "markdown", - "id": "a57a5e8c", + "id": "ff0c2a46", "metadata": { "editable": true }, @@ -546,7 +557,7 @@ }, { "cell_type": "markdown", - "id": "0a45f635", + "id": "4c8ea78a", "metadata": { "editable": true }, @@ -558,7 +569,7 @@ }, { "cell_type": "markdown", - "id": "ac85531a", + "id": "1d119b3e", "metadata": { "editable": true }, @@ -577,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "69061e11", + "id": "b9782b21", "metadata": { "editable": true }, @@ -589,7 +600,7 @@ }, { "cell_type": "markdown", - "id": "b0ed607f", + "id": "457bd0ae", "metadata": { "editable": true }, @@ -603,7 +614,7 @@ }, { "cell_type": "markdown", - "id": "3ab15499", + "id": "fbc011e0", "metadata": { "editable": true }, @@ -615,7 +626,7 @@ }, { "cell_type": "markdown", - "id": "f1c5e132", + "id": "5bb40600", "metadata": { "editable": true }, @@ -625,7 +636,7 @@ }, { "cell_type": "markdown", - "id": "60f2d431", + "id": "e5aebe0a", "metadata": { "editable": true }, @@ -637,7 +648,7 @@ }, { "cell_type": "markdown", - "id": "572f071d", + "id": "6f243211", "metadata": { "editable": true }, @@ -647,7 +658,7 @@ }, { "cell_type": "markdown", - "id": "89eb115c", + "id": "850e1403", "metadata": { "editable": true }, @@ -659,7 +670,7 @@ }, { "cell_type": "markdown", - "id": "9061bf93", + "id": "86066fab", "metadata": { "editable": true }, @@ -678,32 +689,30 @@ }, { "cell_type": "markdown", - "id": "1df7ce8d", + "id": "aedb0de8", "metadata": { "editable": true }, "source": [ "### Part f): Cross-validation as resampling techniques, adding more complexity\n", "\n", - "The aim here is to write your own code for another widely popular\n", + "The aim here is to implement another widely popular\n", "resampling technique, the so-called cross-validation method. \n", "\n", "Implement the $k$-fold cross-validation algorithm (write your own\n", - "code) and evaluate again the MSE function resulting\n", - "from the test folds. You can compare your own code with that from\n", - "**Scikit-Learn** if needed. \n", + "code or use the functionality of **Scikit-Learn**) and evaluate again the MSE function resulting\n", + "from the test folds. \n", "\n", "Compare the MSE you get from your cross-validation code with the one\n", "you got from your **bootstrap** code. Comment your results. Try $5-10$\n", - "folds. You can also compare your own cross-validation code with the\n", - "one provided by **Scikit-Learn**.\n", + "folds. \n", "\n", "In addition to using the ordinary least squares method, you should include both Ridge and Lasso regression." ] }, { "cell_type": "markdown", - "id": "f99f3b0c", + "id": "09e42708", "metadata": { "editable": true }, @@ -731,7 +740,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "d9d2d89a", + "id": "a7412176", "metadata": { "collapsed": false, "editable": true @@ -743,7 +752,7 @@ }, { "cell_type": "markdown", - "id": "59f5b1a6", + "id": "2462a733", "metadata": { "editable": true }, @@ -755,7 +764,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "ab7fd531", + "id": "e299ff99", "metadata": { "collapsed": false, "editable": true @@ -781,7 +790,7 @@ }, { "cell_type": "markdown", - "id": "271b3252", + "id": "58bfbdc9", "metadata": { "editable": true }, @@ -806,7 +815,7 @@ }, { "cell_type": "markdown", - "id": "6555e3ff", + "id": "5c69b9d7", 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See also the grading suggestion at .\n", + "We want you to answer the three different projects by handing in\n", + "reports written like a standard scientific/technical report. The link\n", + "at\n", + "\n", + "gives some guidance. See also the grading suggestion at\n", + ".\n", "\n", - "When using codes from different sources that you have not developed yourself,\n", - "you should refer to these in the bibliography of your report, indicating wherefrom you\n", + "Furthermore, at\n", + "\n", + "you can find examples of previous reports. How to write reports will\n", + "also be discussed during the various lab sessions. Please do ask us if you are in doubt.\n", + "\n", + "When using codes and material from other sources, you should refer to these in the bibliography of your report, indicating wherefrom you for example\n", "got the code, whether this is from the lecture notes, softwares like\n", "Scikit-Learn, TensorFlow, PyTorch or other sources. These should\n", "always be cited correctly. How to cite some of the libraries is often\n", "indicated from their corresponding GitHub sites or websites, see for example how to cite Scikit-Learn at . \n", "\n", "We enocurage you to use tools like\n", - "[ChatGPT](https://openai.com/chatgpt/) in writing the report. If you use for example ChatGPT,\n", + "[ChatGPT](https://openai.com/chatgpt/) or similar in writing the report. If you use for example ChatGPT,\n", "please do cite it properly and include (if possible) your questions and answers as an addition to the report. This can\n", - "be uplodaed to for example your website, GitHub/GitLab or similar as supplemental material.\n", - "\n", - "On scaling, we recommend reading the following section from the scikit-learn software description, see " + "be uploaded to for example your website, GitHub/GitLab or similar as supplemental material." ] }, { "cell_type": "markdown", - "id": "131f84dc", + "id": "eba3be6a", "metadata": { "editable": true }, @@ -64,14 +70,7 @@ "The main aim of this project is to study in more detail various\n", "regression methods, including the Ordinary Least Squares (OLS) method.\n", "In addition to the scientific part, in this course we want also to\n", - "give you an experience in writing scientific reports. The format for\n", - "the delivery of your answers is namely that of a scientific report. At\n", - "for example\n", - "\n", - "we detail how to write a report. Furthermore, at\n", - "\n", - "you can find examples of previous reports. How to write reports will\n", - "also be discussed during the various lab sessions.\n", + "give you an experience in writing scientific reports.\n", "\n", "**A small recommendation when developing the codes here**. Instead of\n", "jumping on to the two-dimensional function described below, we\n", @@ -90,7 +89,7 @@ }, { "cell_type": "markdown", - "id": "335be2ea", + "id": "50da25e5", "metadata": { "editable": true }, @@ -112,7 +111,7 @@ }, { "cell_type": "markdown", - "id": "a77e527b", + "id": "7757fb0c", "metadata": { "editable": true }, @@ -127,7 +126,7 @@ }, { "cell_type": "markdown", - "id": "071b756f", + "id": "83fbdb79", "metadata": { "editable": true }, @@ -160,7 +159,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "eae160df", + "id": "39b900ad", "metadata": { "collapsed": false, "editable": true @@ -212,7 +211,17 @@ }, { "cell_type": "markdown", - "id": "c2cfaf54", + "id": "ef99fea4", + "metadata": { + "editable": true + }, + "source": [ + "If you wish to compare your results with other on the Franke function or other popular functions tested with linear regression, see the list in Figure 1 of the article by Cook et al at ." + ] + }, + { + "cell_type": "markdown", + "id": "b1a7f51d", "metadata": { "editable": true }, @@ -234,7 +243,7 @@ }, { "cell_type": "markdown", - "id": "a7cd4d8f", + "id": "d4bd58e3", "metadata": { "editable": true }, @@ -247,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "c0bbdb51", + "id": "32f767b4", "metadata": { "editable": true }, @@ -259,7 +268,7 @@ }, { "cell_type": "markdown", - "id": "ddee9b4b", + "id": "bae0d9d6", "metadata": { "editable": true }, @@ -271,7 +280,7 @@ }, { "cell_type": "markdown", - "id": "6f220536", + "id": "8f0b2ec9", "metadata": { "editable": true }, @@ -281,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "feb5938f", + "id": "8a455920", "metadata": { "editable": true }, @@ -293,7 +302,7 @@ }, { "cell_type": "markdown", - "id": "8ca0b371", + "id": "3f7ffe3e", "metadata": { "editable": true }, @@ -317,12 +326,14 @@ "approximately $2/3$ to $4/5$ of the data as training data.\n", "\n", "You can easily reuse the solutions to your exercises from week 35 and week 36.\n", - "See also the lecture slides from week 35 and week 36." + "See also the lecture slides from week 35 and week 36.\n", + "\n", + "On scaling, we recommend reading the following section from the scikit-learn software description, see ." ] }, { "cell_type": "markdown", - "id": "17494425", + "id": "cf4d70a4", "metadata": { "editable": true }, @@ -340,7 +351,7 @@ }, { "cell_type": "markdown", - "id": "79411fbf", + "id": "8cc60702", "metadata": { "editable": true }, @@ -357,7 +368,7 @@ }, { "cell_type": "markdown", - "id": "0de10c86", + "id": "bd9d1dd3", "metadata": { "editable": true }, @@ -373,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "b284b2d9", + "id": "03fab7b5", "metadata": { "editable": true }, @@ -385,7 +396,7 @@ }, { "cell_type": "markdown", - "id": "3a862737", + "id": "009b7fb9", "metadata": { "editable": true }, @@ -396,7 +407,7 @@ }, { "cell_type": "markdown", - "id": "8ca87494", + "id": "5bf0a0d5", "metadata": { "editable": true }, @@ -408,7 +419,7 @@ }, { "cell_type": "markdown", - "id": "a253c39b", + "id": "52c48acb", "metadata": { "editable": true }, @@ -420,7 +431,7 @@ }, { "cell_type": "markdown", - "id": "9c38fb19", + "id": "3158357a", "metadata": { "editable": true }, @@ -432,7 +443,7 @@ }, { "cell_type": "markdown", - "id": "8d59d872", + "id": "021253bc", "metadata": { "editable": true }, @@ -443,7 +454,7 @@ }, { "cell_type": "markdown", - "id": "0efd8089", + "id": "9e89d5fe", "metadata": { "editable": true }, @@ -455,7 +466,7 @@ }, { "cell_type": "markdown", - "id": "1a8121b6", + "id": "5f79916c", "metadata": { "editable": true }, @@ -468,7 +479,7 @@ }, { "cell_type": "markdown", - "id": "544da2b4", + "id": "a6e62eff", "metadata": { "editable": true }, @@ -480,7 +491,7 @@ }, { "cell_type": "markdown", - "id": "73fd411a", + "id": "7e833f14", "metadata": { "editable": true }, @@ -490,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "b7160507", + "id": "14ef5a97", "metadata": { "editable": true }, @@ -502,7 +513,7 @@ }, { "cell_type": "markdown", - "id": "b18ae3c3", + "id": "a9443b1d", "metadata": { "editable": true }, @@ -513,7 +524,7 @@ }, { "cell_type": "markdown", - "id": "a57a5e8c", + "id": "ff0c2a46", "metadata": { "editable": true }, @@ -546,7 +557,7 @@ }, { "cell_type": "markdown", - "id": "0a45f635", + "id": "4c8ea78a", "metadata": { "editable": true }, @@ -558,7 +569,7 @@ }, { "cell_type": "markdown", - "id": "ac85531a", + "id": "1d119b3e", "metadata": { "editable": true }, @@ -577,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "69061e11", + "id": "b9782b21", "metadata": { "editable": true }, @@ -589,7 +600,7 @@ }, { "cell_type": "markdown", - "id": "b0ed607f", + "id": "457bd0ae", "metadata": { "editable": true }, @@ -603,7 +614,7 @@ }, { "cell_type": "markdown", - "id": "3ab15499", + "id": "fbc011e0", "metadata": { "editable": true }, @@ -615,7 +626,7 @@ }, { "cell_type": "markdown", - "id": "f1c5e132", + "id": "5bb40600", "metadata": { "editable": true }, @@ -625,7 +636,7 @@ }, { "cell_type": "markdown", - "id": "60f2d431", + "id": "e5aebe0a", "metadata": { "editable": true }, @@ -637,7 +648,7 @@ }, { "cell_type": "markdown", - "id": "572f071d", + "id": "6f243211", "metadata": { "editable": true }, @@ -647,7 +658,7 @@ }, { "cell_type": "markdown", - "id": "89eb115c", + "id": "850e1403", "metadata": { "editable": true }, @@ -659,7 +670,7 @@ }, { "cell_type": "markdown", - "id": "9061bf93", + "id": "86066fab", "metadata": { "editable": true }, @@ -678,32 +689,30 @@ }, { "cell_type": "markdown", - "id": "1df7ce8d", + "id": "aedb0de8", "metadata": { "editable": true }, "source": [ "### Part f): Cross-validation as resampling techniques, adding more complexity\n", "\n", - "The aim here is to write your own code for another widely popular\n", + "The aim here is to implement another widely popular\n", "resampling technique, the so-called cross-validation method. \n", "\n", "Implement the $k$-fold cross-validation algorithm (write your own\n", - "code) and evaluate again the MSE function resulting\n", - "from the test folds. You can compare your own code with that from\n", - "**Scikit-Learn** if needed. \n", + "code or use the functionality of **Scikit-Learn**) and evaluate again the MSE function resulting\n", + "from the test folds. \n", "\n", "Compare the MSE you get from your cross-validation code with the one\n", "you got from your **bootstrap** code. Comment your results. Try $5-10$\n", - "folds. You can also compare your own cross-validation code with the\n", - "one provided by **Scikit-Learn**.\n", + "folds. \n", "\n", "In addition to using the ordinary least squares method, you should include both Ridge and Lasso regression." ] }, { "cell_type": "markdown", - "id": "f99f3b0c", + "id": "09e42708", "metadata": { "editable": true }, @@ -731,7 +740,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "d9d2d89a", + "id": "a7412176", "metadata": { "collapsed": false, "editable": true @@ -743,7 +752,7 @@ }, { "cell_type": "markdown", - "id": "59f5b1a6", + "id": "2462a733", "metadata": { "editable": true }, @@ -755,7 +764,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "ab7fd531", + "id": "e299ff99", "metadata": { "collapsed": false, "editable": true @@ -781,7 +790,7 @@ }, { "cell_type": "markdown", - "id": "271b3252", + "id": "58bfbdc9", "metadata": { "editable": true }, @@ -806,7 +815,7 @@ }, { "cell_type": "markdown", - "id": "6555e3ff", + "id": "5c69b9d7", "metadata": { "editable": true }, @@ -820,7 +829,7 @@ }, { "cell_type": "markdown", - "id": "8289021b", + "id": "a92b1a41", "metadata": { "editable": true }, @@ -850,7 +859,7 @@ }, { "cell_type": "markdown", - "id": "4349e2d3", + "id": "3da35987", "metadata": { "editable": true }, @@ -872,7 +881,7 @@ }, { "cell_type": "markdown", - "id": "274e1581", + "id": "c03bf204", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/html/genindex.html b/doc/LectureNotes/_build/html/genindex.html index 1a9ebe33d..fb71b3ddc 100644 --- a/doc/LectureNotes/_build/html/genindex.html +++ b/doc/LectureNotes/_build/html/genindex.html @@ -282,7 +282,7 @@ const thebe_selector_output = ".output, .cell_output"

  • - + Project 1 on Machine Learning, deadline October 7 (midnight), 2024
    • diff --git a/doc/LectureNotes/_build/html/intro.html b/doc/LectureNotes/_build/html/intro.html index f612b321a..e16339b03 100644 --- a/doc/LectureNotes/_build/html/intro.html +++ b/doc/LectureNotes/_build/html/intro.html @@ -283,7 +283,7 @@ const thebe_selector_output = ".output, .cell_output"

    • - + Project 1 on Machine Learning, deadline October 7 (midnight), 2024
      • diff --git a/doc/LectureNotes/_build/html/objects.inv b/doc/LectureNotes/_build/html/objects.inv index c1742b0606769d8b4b805d3dae2d2c137f397d17..cf5c7f574f8b6248039ca634252226ac83ecf54e 100644 GIT binary patch delta 872 zcmV-u1DE{T2i^yegMShdAh$rE9|KJWXnR)d#a3fWt|TXTNPmn5*m9G>9I=FZ5-R9w zOFInF4jt`{6dRdWmZTQc)umg8QHWvW7#=aMlobTRp@m0JVX;BQRGeAe8;;wCFC@t8 zdE`loz(fM~OluiPct}etMYE#Jt-A*_nM~RU&I6!xUs;)QZFn4TH4dQ0z8qCqN@d_D z#zrc5U^YtB1Ama6m>Dlx1(N{R#Bt3r=GHA3g&$E|1Sl>X#jVg-ta5DPp2D4y1-0E1 zk&yYBn88TWDR%XPu5*0*N-hh8YbhxOA{`JO4VVn!TU7()%YG^^11c_kyE9FBu9*)==sM7{NpbF y*st@2q5v4><=|_?y8PhPc{twPsdi2#)0Q^HhIYRIG_$Bmg~ok$ME?Ll%0*;bW}t2W delta 868 zcmV-q1DpKb2igaagMR`EkXs9Ko`&eadW+w@+{cTEVS|GpP`6P>_-;9YKO3$(`vgwt9sGr?6MZ zM9DO>`y&`1j!&XE6QxBOIB9L($qh3If_qE}jgeY)n8Oh0(0`*YDw!u71qirCOyFA6 zMo7ZA%P#efb{wJ| zd)iB_);g~oNgYr>F4Hj_hZv4M!#&0oxqv`jzWU`09mS76=zwa|xVUZV@fm4B=Z<1NGBxDSyubQJgj7E)3UEvf_*bI+`U( z70Xh%Nh?aN+?>pL)V4+%*jrxC_gmic0Pnfy{XQJ57NJWk3;DNJKmgo7_B?kz%cu@7 z=6D=+DY%y$vMbN-3IUO)+-lZW5yg3BnA%zU4m)cjs@Get%0HB;N~EEy zL}Ke%3r#$lTb^shi&@x;g&!3<38YeP584Co;(t~r6$`^B)jRzUtnoHLn>fSzv6oG2 z^QcD@eh+k4>pjmYJsYRzLby2-d1TOSjA0Dk*6b^Pz7#1&#`LqB9b98< zfnCjG^j0{1z5MbjnQ^Uo&XOvw*96zB*1fLC;HX>vK8z)axXwmei4Fhc4gaLiKWR4Y u9dQDJ>+ - @@ -541,33 +541,32 @@ doconce format html Project1.do.txt -->

      Date: September 2

      Preamble: Note on writing reports, using reference material, AI and other tools¶

      -

      We want you to answer the three different projects by handing reports written like a standard scientific/technical report. -The link at https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ProjectWriting/projectwriting.ipynb gives some guidance. See also the grading suggestion at https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md.

      -

      When using codes from different sources that you have not developed yourself, -you should refer to these in the bibliography of your report, indicating wherefrom you +

      We want you to answer the three different projects by handing in +reports written like a standard scientific/technical report. The link +at +https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ProjectWriting/projectwriting.ipynb +gives some guidance. See also the grading suggestion at +https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md.

      +

      Furthermore, at +https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ +you can find examples of previous reports. How to write reports will +also be discussed during the various lab sessions. Please do ask us if you are in doubt.

      +

      When using codes and material from other sources, you should refer to these in the bibliography of your report, indicating wherefrom you for example got the code, whether this is from the lecture notes, softwares like Scikit-Learn, TensorFlow, PyTorch or other sources. These should always be cited correctly. How to cite some of the libraries is often indicated from their corresponding GitHub sites or websites, see for example how to cite Scikit-Learn at https://scikit-learn.org/dev/about.html.

      We enocurage you to use tools like -ChatGPT in writing the report. If you use for example ChatGPT, +ChatGPT or similar in writing the report. If you use for example ChatGPT, please do cite it properly and include (if possible) your questions and answers as an addition to the report. This can -be uplodaed to for example your website, GitHub/GitLab or similar as supplemental material.

      -

      On scaling, we recommend reading the following section from the scikit-learn software description, see https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section

      +be uploaded to for example your website, GitHub/GitLab or similar as supplemental material.

      Regression analysis and resampling methods¶

      The main aim of this project is to study in more detail various regression methods, including the Ordinary Least Squares (OLS) method. In addition to the scientific part, in this course we want also to -give you an experience in writing scientific reports. The format for -the delivery of your answers is namely that of a scientific report. At -for example -https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md -we detail how to write a report. Furthermore, at -https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ -you can find examples of previous reports. How to write reports will -also be discussed during the various lab sessions.

      +give you an experience in writing scientific reports.

      A small recommendation when developing the codes here. Instead of jumping on to the two-dimensional function described below, we recommend to do the code development and testing with a simpler @@ -682,6 +681,7 @@ which polynomial fits the data best.

      +

      If you wish to compare your results with other on the Franke function or other popular functions tested with linear regression, see the list in Figure 1 of the article by Cook et al at https://arxiv.org/abs/2401.11694.

      Part a) : Ordinary Least Square (OLS) on the Franke function¶

      @@ -728,6 +728,7 @@ data and say test data. An accepted rule of thumb is to use approximately \(2/3\) to \(4/5\) of the data as training data.

      You can easily reuse the solutions to your exercises from week 35 and week 36. See also the lecture slides from week 35 and week 36.

      +

      On scaling, we recommend reading the following section from the scikit-learn software description, see https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section.

      Part b): Adding Ridge regression for the Franke function¶

      @@ -854,16 +855,14 @@ You can follow the code example in the jupyter-book at

      Part f): Cross-validation as resampling techniques, adding more complexity¶

      -

      The aim here is to write your own code for another widely popular +

      The aim here is to implement another widely popular resampling technique, the so-called cross-validation method.

      Implement the \(k\)-fold cross-validation algorithm (write your own -code) and evaluate again the MSE function resulting -from the test folds. You can compare your own code with that from -Scikit-Learn if needed.

      +code or use the functionality of Scikit-Learn) and evaluate again the MSE function resulting +from the test folds.

      Compare the MSE you get from your cross-validation code with the one you got from your bootstrap code. Comment your results. Try \(5-10\) -folds. You can also compare your own cross-validation code with the -one provided by Scikit-Learn.

      +folds.

      In addition to using the ordinary least squares method, you should include both Ridge and Lasso regression.

      diff --git a/doc/LectureNotes/_build/html/search.html b/doc/LectureNotes/_build/html/search.html index b4dc25895..ed586d23e 100644 --- a/doc/LectureNotes/_build/html/search.html +++ b/doc/LectureNotes/_build/html/search.html @@ -288,7 +288,7 @@ const thebe_selector_output = ".output, .cell_output"

      • - + Project 1 on Machine Learning, deadline October 7 (midnight), 2024
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es:["Project 1 on Machine Learning, deadline October 7 (midnight), 2024","3. Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Rgeression and Statistical 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Rgeression and Statistical interpretations"],titleterms:{"1":[0,15,16,17,20,25,26],"17":[],"18":[],"2":[0,15,16,17,25,26,27],"2023":23,"2024":20,"26":26,"3":[0,15,16,25,26],"34":[15,25],"35":[16,26],"36":[17,27],"4":[0,26],"5":0,"7":20,"9":[],"case":[8,10,22,26,27],"do":[1,27],"final":[12,26,27],"function":[0,1,6,7,8,10,11,12,13,20,22,25,26,27],"import":[5,19,25,26,27],"new":[4,27],A:[0,1,4,8,9,25,27],And:[25,26,27],For:26,In:23,Ising:6,The:[0,1,2,3,5,6,7,8,9,11,12,17,18,25,26,27],To:[25,26],With:[4,27],about:[25,26],abov:27,activ:[1,12,27],ad:[0,6,17,20,25,26],adaboost:10,adagrad:13,adam:13,adapt:10,adjust:1,adversari:4,again:[3,9],ai:[20,25],aim:[8,9,17,25],aka:[25,26],algebra:[19,25],algorithm:[9,10,11,12,26],algortithm:13,all:8,altern:[],an:[0,4,10,25],analys:[5,26],analysi:[0,5,6,11,18,20,22,25,26,27],analyt:[0,16,17],analyz:[],ani:13,anoth:[9,27],appli:18,approach:[0,8,14,25],approxim:12,architectur:1,arrai:[19,25],assist:23,assumpt:27,august:26,autocorrel:22,autograd:[2,13],automat:13,b:[17,20],back:[1,11,12],background:[18,20],bag:10,base:13,basic:[0,5,7,9,10,11,19,26,27],batch:1,bay:[5,27],befor:11,beta:27,better:8,bia:[6,20],binari:1,bind:25,bird:10,boldsymbol:[26,27],boost:10,bootstrap:[6,10],boston:0,breast:1,brief:25,bring:12,build:[1,3,9],c:[20,25],calcul:26,can:25,cancer:[1,7,9,11],cart:9,center:26,central:[13,18,22],chain:12,chang:10,channel:25,chi:[0,25],choos:1,cifar01:3,classic:11,classif:[1,9,10],classifi:8,clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,25,26,27],collect:[1,3],commun:25,compar:[2,10],comparison:27,complet:26,complex:[0,6,20,26],complic:6,compon:11,comput:9,computerlab:25,con:9,concept:22,condit:27,conjug:13,contn:25,convex:[8,13],convolut:[3,12],correctli:27,correl:[11,26],cost:[1,10,26,27],cours:[18,24,25],covari:[5,11,22,26],cover:25,critic:[],cross:[6,20],cython:25,d:20,data:[0,1,3,6,7,9,11,15,16,18,20,22,25,26,27],dataset:[1,3],deadlin:[20,25],decai:2,decemb:[],decis:[9,10],decomposit:[5,11,17,19,26],deep:[1,2,25],defin:[1,25],degre:[0,26],deliveri:20,dens:[0,25],deriv:[5,12,26,27],descent:[2,10,13],descript:20,design:26,detail:[3,25],develop:1,diagon:11,differ:8,differenti:[2,13],diffus:2,dimension:[2,3,8,20,26],disadvantag:9,discret:22,distribut:[5,22,27],doe:[26,27],domain:22,down:1,dropout:1,e:20,economi:26,electron:20,element:[0,22,25],elimin:19,energi:25,ensembl:10,entropi:9,environ:[0,15,25],equat:[0,2,12,25,26,27],error:[0,10,25,26],essenti:25,estim:27,etc:25,euler:2,evalu:1,exampl:[0,1,2,3,4,6,7,8,9,10,25,26,27],exercis:[0,6,15,16,17,25,26],expect:[22,27],experi:22,explor:[0,15,16,25],exponenti:2,express:[17,26],extrapol:4,extrem:[10,25],ey:10,f:20,fall:23,famili:[1,25,26],famou:19,fantast:26,featur:[9,19,26],feed:[1,12],fine:1,first:[4,12,25,26,27],fit:[0,10,25,27],fix:26,forc:3,forest:10,format:[20,25],forward:[1,2,12],fourier:3,frank:[6,20,26],freedom:[0,26],frequent:26,frequentist:[0,25],from:[5,10,12,26,27],full:2,further:[3,5,26],g:20,gan:4,gaussian:19,gd:13,gener:[4,9,25],geometr:11,gini:9,good:[0,25],grade:[23,25],gradient:[1,2,10,13],growth:2,ha:18,handl:[19,25,26],happen:27,hessian:26,hidden:2,hous:0,hyperparamet:1,hyperplan:8,i:1,id3:9,idea:11,ident:27,ii:25,iid:27,illustr:27,implement:1,implic:[5,26],improv:1,includ:13,increment:11,independ:27,index:9,inform:23,input:2,instal:[18,20,25],instructor:23,intercept:26,interpret:[5,11,25,26,27],introduc:[11,13,26],introduct:[0,6,18,19,20,25],invers:[5,19,27],invert:26,iter:10,its:26,jacobian:26,jax:13,julia:25,jungl:10,kera:[1,3],kernel:[8,11],lagrangian:8,lasso:[5,6,20,26,27],last:26,later:[5,26],layer:[1,2,3,12],learn:[0,1,2,11,13,14,15,16,18,20,25,26,27],least:[5,6,20,25,26,27],lectur:[25,26,27],level:10,librari:[18,25],likelihood:[7,27],limit:[1,13,22],linear:[0,8,13,19,25,26,27],link:[5,11,21,24,26,27],literatur:20,logist:[7,25],loss:26,lu:19,machin:[0,8,13,18,20,25],made:27,main:[22,25],make:[0,9,10,15,16,25,26],mani:[10,12],manipul:26,margin:27,mass:25,materi:[20,21,25,26,27],math:[5,26],mathemat:[3,5,8,26],matric:[5,19,25,27],matrix:[1,5,11,12,19,25,26,27],matter:[0,25],max:26,maximum:27,mean:[0,26,27],meet:[5,10,22,25,26],mercer:8,method:[6,9,10,13,20,25],midnight:20,min:26,minim:25,ml:25,mle:27,mlp:12,mnist:[3,4],model:[0,1,4,6,12,25],momentum:13,mondai:[26,27],moon:[8,9],more:[3,6,19,20,25,26,27],multilay:12,multipl:[1,3],multipli:8,need:[20,25],network:[1,2,3,4,7,12,25],neural:[1,2,3,4,7,12,25],non:8,normal:[0,1],notat:12,note:[20,26,27],novemb:[],now:[1,9,13,27],nuclear:[0,25],numba:25,number:[0,2,22,26],numer:[2,20,22],numpi:[19,25],object:3,obtain:11,octob:20,od:2,off:[6,20],ol:[5,6,20,27],one:[2,12],oper:19,optim:[1,8,13,18,25,26],order:13,ordinari:[5,6,20,25,26,27],organ:[0,25],oslo:24,other:[4,9,11,12,19,20,25,26],our:[0,4,5,11,13,25,26],outcom:[18,25],output:2,overarch:[0,4,8,9,17,25,26],overview:[10,25],own:[0,10,11,15,16,25,26],packag:[19,25],panda:[25,26],paper:20,paramet:[25,26],part:[13,18,20,26],partial:2,pass:1,path:[],pca:11,pdf:22,pencil:20,perceptron:12,perform:[1,9],period:3,perspect:1,plan:[26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\ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb index 8dab92163..5a9f12593 100644 --- a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f29d1e5a", + "id": "6c8c59f8", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "60f7a134", + "id": "24af4cf5", "metadata": { "editable": true }, @@ -27,34 +27,40 @@ }, { "cell_type": "markdown", - "id": "43b931d0", + "id": "2971d68f", "metadata": { "editable": true }, "source": [ "## Preamble: Note on writing reports, using reference material, AI and other tools\n", "\n", - "We want you to answer the three different projects by handing reports written like a standard scientific/technical report.\n", - "The link at gives some guidance. See also the grading suggestion at .\n", + "We want you to answer the three different projects by handing in\n", + "reports written like a standard scientific/technical report. The link\n", + "at\n", + "\n", + "gives some guidance. See also the grading suggestion at\n", + ".\n", "\n", - "When using codes from different sources that you have not developed yourself,\n", - "you should refer to these in the bibliography of your report, indicating wherefrom you\n", + "Furthermore, at\n", + "\n", + "you can find examples of previous reports. How to write reports will\n", + "also be discussed during the various lab sessions. Please do ask us if you are in doubt.\n", + "\n", + "When using codes and material from other sources, you should refer to these in the bibliography of your report, indicating wherefrom you for example\n", "got the code, whether this is from the lecture notes, softwares like\n", "Scikit-Learn, TensorFlow, PyTorch or other sources. These should\n", "always be cited correctly. How to cite some of the libraries is often\n", "indicated from their corresponding GitHub sites or websites, see for example how to cite Scikit-Learn at . \n", "\n", "We enocurage you to use tools like\n", - "[ChatGPT](https://openai.com/chatgpt/) in writing the report. If you use for example ChatGPT,\n", + "[ChatGPT](https://openai.com/chatgpt/) or similar in writing the report. If you use for example ChatGPT,\n", "please do cite it properly and include (if possible) your questions and answers as an addition to the report. This can\n", - "be uplodaed to for example your website, GitHub/GitLab or similar as supplemental material.\n", - "\n", - "On scaling, we recommend reading the following section from the scikit-learn software description, see " + "be uploaded to for example your website, GitHub/GitLab or similar as supplemental material." ] }, { "cell_type": "markdown", - "id": "131f84dc", + "id": "eba3be6a", "metadata": { "editable": true }, @@ -64,14 +70,7 @@ "The main aim of this project is to study in more detail various\n", "regression methods, including the Ordinary Least Squares (OLS) method.\n", "In addition to the scientific part, in this course we want also to\n", - "give you an experience in writing scientific reports. The format for\n", - "the delivery of your answers is namely that of a scientific report. At\n", - "for example\n", - "\n", - "we detail how to write a report. Furthermore, at\n", - "\n", - "you can find examples of previous reports. How to write reports will\n", - "also be discussed during the various lab sessions.\n", + "give you an experience in writing scientific reports.\n", "\n", "**A small recommendation when developing the codes here**. Instead of\n", "jumping on to the two-dimensional function described below, we\n", @@ -90,7 +89,7 @@ }, { "cell_type": "markdown", - "id": "335be2ea", + "id": "50da25e5", "metadata": { "editable": true }, @@ -112,7 +111,7 @@ }, { "cell_type": "markdown", - "id": "a77e527b", + "id": "7757fb0c", "metadata": { "editable": true }, @@ -127,7 +126,7 @@ }, { "cell_type": "markdown", - "id": "071b756f", + "id": "83fbdb79", "metadata": { "editable": true }, @@ -160,7 +159,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "eae160df", + "id": "39b900ad", "metadata": { "collapsed": false, "editable": true @@ -233,7 +232,17 @@ }, { "cell_type": "markdown", - "id": "c2cfaf54", + "id": "ef99fea4", + "metadata": { + "editable": true + }, + "source": [ + "If you wish to compare your results with other on the Franke function or other popular functions tested with linear regression, see the list in Figure 1 of the article by Cook et al at ." + ] + }, + { + "cell_type": "markdown", + "id": "b1a7f51d", "metadata": { "editable": true }, @@ -255,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "a7cd4d8f", + "id": "d4bd58e3", "metadata": { "editable": true }, @@ -268,7 +277,7 @@ }, { "cell_type": "markdown", - "id": "c0bbdb51", + "id": "32f767b4", "metadata": { "editable": true }, @@ -280,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "ddee9b4b", + "id": "bae0d9d6", "metadata": { "editable": true }, @@ -292,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "6f220536", + "id": "8f0b2ec9", "metadata": { "editable": true }, @@ -302,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "feb5938f", + "id": "8a455920", "metadata": { "editable": true }, @@ -314,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "8ca0b371", + "id": "3f7ffe3e", "metadata": { "editable": true }, @@ -338,12 +347,14 @@ "approximately $2/3$ to $4/5$ of the data as training data.\n", "\n", "You can easily reuse the solutions to your exercises from week 35 and week 36.\n", - "See also the lecture slides from week 35 and week 36." + "See also the lecture slides from week 35 and week 36.\n", + "\n", + "On scaling, we recommend reading the following section from the scikit-learn software description, see ." ] }, { "cell_type": "markdown", - "id": "17494425", + "id": "cf4d70a4", "metadata": { "editable": true }, @@ -361,7 +372,7 @@ }, { "cell_type": "markdown", - "id": "79411fbf", + "id": "8cc60702", "metadata": { "editable": true }, @@ -378,7 +389,7 @@ }, { "cell_type": "markdown", - "id": "0de10c86", + "id": "bd9d1dd3", "metadata": { "editable": true }, @@ -394,7 +405,7 @@ }, { "cell_type": "markdown", - "id": "b284b2d9", + "id": "03fab7b5", "metadata": { "editable": true }, @@ -406,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "3a862737", + "id": "009b7fb9", "metadata": { "editable": true }, @@ -417,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "8ca87494", + "id": "5bf0a0d5", "metadata": { "editable": true }, @@ -429,7 +440,7 @@ }, { "cell_type": "markdown", - "id": "a253c39b", + "id": "52c48acb", "metadata": { "editable": true }, @@ -441,7 +452,7 @@ }, { "cell_type": "markdown", - "id": "9c38fb19", + "id": "3158357a", "metadata": { "editable": true }, @@ -453,7 +464,7 @@ }, { "cell_type": "markdown", - "id": "8d59d872", + "id": "021253bc", "metadata": { "editable": true }, @@ -464,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "0efd8089", + "id": "9e89d5fe", "metadata": { "editable": true }, @@ -476,7 +487,7 @@ }, { "cell_type": "markdown", - "id": "1a8121b6", + "id": "5f79916c", "metadata": { "editable": true }, @@ -489,7 +500,7 @@ }, { "cell_type": "markdown", - "id": "544da2b4", + "id": "a6e62eff", "metadata": { "editable": true }, @@ -501,7 +512,7 @@ }, { "cell_type": "markdown", - "id": "73fd411a", + "id": "7e833f14", "metadata": { "editable": true }, @@ -511,7 +522,7 @@ }, { "cell_type": "markdown", - "id": "b7160507", + "id": "14ef5a97", "metadata": { "editable": true }, @@ -523,7 +534,7 @@ }, { "cell_type": "markdown", - "id": "b18ae3c3", + "id": "a9443b1d", "metadata": { "editable": true }, @@ -534,7 +545,7 @@ }, { "cell_type": "markdown", - "id": "a57a5e8c", + "id": "ff0c2a46", "metadata": { "editable": true }, @@ -567,7 +578,7 @@ }, { "cell_type": "markdown", - "id": "0a45f635", + "id": "4c8ea78a", "metadata": { "editable": true }, @@ -579,7 +590,7 @@ }, { "cell_type": "markdown", - "id": "ac85531a", + "id": "1d119b3e", "metadata": { "editable": true }, @@ -598,7 +609,7 @@ }, { "cell_type": "markdown", - "id": "69061e11", + "id": "b9782b21", "metadata": { "editable": true }, @@ -610,7 +621,7 @@ }, { "cell_type": "markdown", - "id": "b0ed607f", + "id": "457bd0ae", "metadata": { "editable": true }, @@ -624,7 +635,7 @@ }, { "cell_type": "markdown", - "id": "3ab15499", + "id": "fbc011e0", "metadata": { "editable": true }, @@ -636,7 +647,7 @@ }, { "cell_type": "markdown", - "id": "f1c5e132", + "id": "5bb40600", "metadata": { "editable": true }, @@ -646,7 +657,7 @@ }, { "cell_type": "markdown", - "id": "60f2d431", + "id": "e5aebe0a", "metadata": { "editable": true }, @@ -658,7 +669,7 @@ }, { "cell_type": "markdown", - "id": "572f071d", + "id": "6f243211", "metadata": { "editable": true }, @@ -668,7 +679,7 @@ }, { "cell_type": "markdown", - "id": "89eb115c", + "id": "850e1403", "metadata": { "editable": true }, @@ -680,7 +691,7 @@ }, { "cell_type": "markdown", - "id": "9061bf93", + "id": "86066fab", "metadata": { "editable": true }, @@ -699,32 +710,30 @@ }, { "cell_type": "markdown", - "id": "1df7ce8d", + "id": "aedb0de8", "metadata": { "editable": true }, "source": [ "### Part f): Cross-validation as resampling techniques, adding more complexity\n", "\n", - "The aim here is to write your own code for another widely popular\n", + "The aim here is to implement another widely popular\n", "resampling technique, the so-called cross-validation method. \n", "\n", "Implement the $k$-fold cross-validation algorithm (write your own\n", - "code) and evaluate again the MSE function resulting\n", - "from the test folds. You can compare your own code with that from\n", - "**Scikit-Learn** if needed. \n", + "code or use the functionality of **Scikit-Learn**) and evaluate again the MSE function resulting\n", + "from the test folds. \n", "\n", "Compare the MSE you get from your cross-validation code with the one\n", "you got from your **bootstrap** code. Comment your results. Try $5-10$\n", - "folds. You can also compare your own cross-validation code with the\n", - "one provided by **Scikit-Learn**.\n", + "folds. \n", "\n", "In addition to using the ordinary least squares method, you should include both Ridge and Lasso regression." ] }, { "cell_type": "markdown", - "id": "f99f3b0c", + "id": "09e42708", "metadata": { "editable": true }, @@ -752,7 +761,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "d9d2d89a", + "id": "a7412176", "metadata": { "collapsed": false, "editable": true @@ -764,7 +773,7 @@ }, { "cell_type": "markdown", - "id": "59f5b1a6", + "id": "2462a733", "metadata": { "editable": true }, @@ -776,7 +785,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "ab7fd531", + "id": "e299ff99", "metadata": { "collapsed": false, "editable": true @@ -802,7 +811,7 @@ }, { "cell_type": "markdown", - "id": "271b3252", + "id": "58bfbdc9", "metadata": { "editable": true }, @@ -827,7 +836,7 @@ }, { "cell_type": "markdown", - "id": "6555e3ff", + "id": "5c69b9d7", "metadata": { "editable": true }, @@ -841,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "8289021b", + "id": "a92b1a41", "metadata": { "editable": true }, @@ -871,7 +880,7 @@ }, { "cell_type": "markdown", - "id": "4349e2d3", + "id": "3da35987", "metadata": { "editable": true }, @@ -893,7 +902,7 @@ }, { "cell_type": "markdown", - "id": "274e1581", + "id": "c03bf204", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/jupyter_execute/project1.py b/doc/LectureNotes/_build/jupyter_execute/project1.py index 9a839096b..be7383aa2 100644 --- a/doc/LectureNotes/_build/jupyter_execute/project1.py +++ b/doc/LectureNotes/_build/jupyter_execute/project1.py @@ -12,36 +12,35 @@ # ## Preamble: Note on writing reports, using reference material, AI and other tools # -# We want you to answer the three different projects by handing reports written like a standard scientific/technical report. -# The link at gives some guidance. See also the grading suggestion at . +# We want you to answer the three different projects by handing in +# reports written like a standard scientific/technical report. The link +# at +# +# gives some guidance. See also the grading suggestion at +# . # -# When using codes from different sources that you have not developed yourself, -# you should refer to these in the bibliography of your report, indicating wherefrom you +# Furthermore, at +# +# you can find examples of previous reports. How to write reports will +# also be discussed during the various lab sessions. Please do ask us if you are in doubt. +# +# When using codes and material from other sources, you should refer to these in the bibliography of your report, indicating wherefrom you for example # got the code, whether this is from the lecture notes, softwares like # Scikit-Learn, TensorFlow, PyTorch or other sources. These should # always be cited correctly. How to cite some of the libraries is often # indicated from their corresponding GitHub sites or websites, see for example how to cite Scikit-Learn at . # # We enocurage you to use tools like -# [ChatGPT](https://openai.com/chatgpt/) in writing the report. If you use for example ChatGPT, +# [ChatGPT](https://openai.com/chatgpt/) or similar in writing the report. If you use for example ChatGPT, # please do cite it properly and include (if possible) your questions and answers as an addition to the report. This can -# be uplodaed to for example your website, GitHub/GitLab or similar as supplemental material. -# -# On scaling, we recommend reading the following section from the scikit-learn software description, see +# be uploaded to for example your website, GitHub/GitLab or similar as supplemental material. # ## Regression analysis and resampling methods # # The main aim of this project is to study in more detail various # regression methods, including the Ordinary Least Squares (OLS) method. # In addition to the scientific part, in this course we want also to -# give you an experience in writing scientific reports. The format for -# the delivery of your answers is namely that of a scientific report. At -# for example -# -# we detail how to write a report. Furthermore, at -# -# you can find examples of previous reports. How to write reports will -# also be discussed during the various lab sessions. +# give you an experience in writing scientific reports. # # **A small recommendation when developing the codes here**. Instead of # jumping on to the two-dimensional function described below, we @@ -148,6 +147,8 @@ fig.colorbar(surf, shrink=0.5, aspect=5) plt.show() +# If you wish to compare your results with other on the Franke function or other popular functions tested with linear regression, see the list in Figure 1 of the article by Cook et al at . + # ### Part a) : Ordinary Least Square (OLS) on the Franke function # # We will generate our own dataset for a function @@ -201,6 +202,8 @@ plt.show() # # You can easily reuse the solutions to your exercises from week 35 and week 36. # See also the lecture slides from week 35 and week 36. +# +# On scaling, we recommend reading the following section from the scikit-learn software description, see . # ### Part b): Adding Ridge regression for the Franke function # @@ -352,18 +355,16 @@ plt.show() # ### Part f): Cross-validation as resampling techniques, adding more complexity # -# The aim here is to write your own code for another widely popular +# The aim here is to implement another widely popular # resampling technique, the so-called cross-validation method. # # Implement the $k$-fold cross-validation algorithm (write your own -# code) and evaluate again the MSE function resulting -# from the test folds. You can compare your own code with that from -# **Scikit-Learn** if needed. +# code or use the functionality of **Scikit-Learn**) and evaluate again the MSE function resulting +# from the test folds. # # Compare the MSE you get from your cross-validation code with the one # you got from your **bootstrap** code. Comment your results. Try $5-10$ -# folds. You can also compare your own cross-validation code with the -# one provided by **Scikit-Learn**. +# folds. # # In addition to using the ordinary least squares method, you should include both Ridge and Lasso regression. diff --git a/doc/LectureNotes/_toc.yml b/doc/LectureNotes/_toc.yml index e8c8f91eb..fda6acb21 100644 --- a/doc/LectureNotes/_toc.yml +++ b/doc/LectureNotes/_toc.yml @@ -50,6 +50,6 @@ parts: - caption: Projects numbered: false chapters: - - file: Project1.ipynb + - file: project1.ipynb # - file: project2.ipynb # - file: Project3.ipynb