{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Rappels sur scikit-learn et le machine learning\n", "\n", "Quelques exercices simples sur *scikit-learn*. Le notebook est long pour ceux qui d\u00e9butent en machine learning et sans doute sans suspens pour ceux qui en ont d\u00e9j\u00e0 fait."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"data": {"text/html": ["<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", "    var a = \"\" ;\n", "    for ( ; n > 0 ; --n)\n", "        a += \"    \";\n", "    return a;\n", "}\n", "// look up into all sections and builds an automated menu //\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n", "    var anchors = document.getElementsByClassName(\"section\");\n", "    if (anchors.length == 0) {\n", "        anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", "    }\n", "    var i,t;\n", "    var text_menu = begin;\n", "    var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", "    var ind = \"\";\n", "    var memo_level = 1;\n", "    var href;\n", "    var tags = [];\n", "    var main_item = 0;\n", "    var format_open = 0;\n", "    for (i = 0; i <= llast; i++)\n", "        tags.push(\"h\" + i);\n", "\n", "    for (i = 0; i < anchors.length; i++) {\n", "        text_memo += \"**\" + anchors[i].id + \"--\\n\";\n", "\n", "        var child = null;\n", "        for(t = 0; t < tags.length; t++) {\n", "            var r = anchors[i].getElementsByTagName(tags[t]);\n", "            if (r.length > 0) {\n", "child = r[0];\n", "break;\n", "            }\n", "        }\n", "        if (child == null) {\n", "            text_memo += \"null\\n\";\n", "            continue;\n", "        }\n", "        if (anchors[i].hasAttribute(\"id\")) {\n", "            // when converted in RST\n", "            href = anchors[i].id;\n", "            text_memo += \"#1-\" + href;\n", "            // passer \u00e0 child suivant (le chercher)\n", "        }\n", "        else if (child.hasAttribute(\"id\")) {\n", "            // in a notebook\n", "            href = child.id;\n", "            text_memo += \"#2-\" + href;\n", "        }\n", "        else {\n", "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n", "            continue;\n", "        }\n", "        var title = child.textContent;\n", "        var level = parseInt(child.tagName.substring(1,2));\n", "\n", "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", "        if ((level < lfirst) || (level > llast)) {\n", "            continue ;\n", "        }\n", "        if (title.endsWith('\u00b6')) {\n", "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", "         .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", "        }\n", "        if (title.length == 0) {\n", "            continue;\n", "        }\n", "\n", "        while (level < memo_level) {\n", "            text_menu += end_format + \"</ul>\\n\";\n", "            format_open -= 1;\n", "            memo_level -= 1;\n", "        }\n", "        if (level == lfirst) {\n", "            main_item += 1;\n", "        }\n", "        if (keep_item != -1 && main_item != keep_item + 1) {\n", "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", "            continue;\n", "        }\n", "        while (level > memo_level) {\n", "            text_menu += \"<ul>\\n\";\n", "            memo_level += 1;\n", "        }\n", "        text_menu += repeat_indent_string(level-2);\n", "        text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", "        format_open += 1;\n", "    }\n", "    while (1 < memo_level) {\n", "        text_menu += end_format + \"</ul>\\n\";\n", "        memo_level -= 1;\n", "        format_open -= 1;\n", "    }\n", "    text_menu += send;\n", "    //text_menu += \"\\n\" + text_memo;\n", "\n", "    while (format_open > 0) {\n", "        text_menu += end_format;\n", "        format_open -= 1;\n", "    }\n", "    return text_menu;\n", "};\n", "var update_menu = function() {\n", "    var sbegin = \"\";\n", "    var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", "    var send = \"\";\n", "    var begin_format = '<li>';\n", "    var end_format = '</li>';\n", "    var keep_item = -1;\n", "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", "       begin_format, end_format);\n", "    var menu = document.getElementById(\"my_id_menu_nb\");\n", "    menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", "            </script>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Des donn\u00e9es synth\u00e9tiques\n", "\n", "On simule un jeu de donn\u00e9es al\u00e9atoires."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[0.25324685, 0.97811479],\n", "       [0.32928095, 0.40816327],\n", "       [0.44178633, 0.51600754],\n", "       [0.76893618, 0.34170807],\n", "       [0.00282938, 0.49371721]])"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["from numpy import random\n", "n = 1000\n", "X = random.rand(n, 2)\n", "X[:5]"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([-0.18666718,  1.29326419,  1.64748543,  2.39341326,  0.06048883])"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["y = X[:, 0] * 3 - 2 * X[:, 1] ** 2 + random.rand(n)\n", "y[:5]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 1 : diviser en base d'apprentissage et de test"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 2 : caler une r\u00e9gression lin\u00e9aire\n", "\n", "Et calculer le coefficient $R^2$."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 3 : am\u00e9liorer le mod\u00e8le en appliquant une transformation bien choisie"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 4 : caler une for\u00eat al\u00e9atoire"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 5 : un peu de math\n", "\n", "Comparer les deux mod\u00e8les sur les donn\u00e9es suivantes ? Que remarquez-vous ? Expliquez pourquoi ?"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": ["X_test2 = random.rand(n, 2) + 0.5\n", "y_test2 = X_test2[:, 0] * 3 - 2 * X_test2[:, 1] ** 2 + random.rand(n)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 6 : faire un graphe avec...\n", "\n", "Le nuage de points du premier et second jeu, les pr\u00e9dictions des deux mod\u00e8les, une l\u00e9gende, un titre... avec [pandas](https://pandas.pydata.org/) ou directement avec [matplotlib](https://matplotlib.org/) au choix."]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 7 : illuster l'overfitting avec un arbre de d\u00e9cision\n", "\n", "Sur le premier jeu de donn\u00e9es."]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Exercice 8  : augmenter le nombre de features et r\u00e9gulariser une r\u00e9gression logistique\n", "\n", "L'objectif est de regarder l'impact de la r\u00e9gularisation des coefficients d'une r\u00e9gression logistique lorsque le nombre de features augmentent."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0"}}, "nbformat": 4, "nbformat_minor": 2}