2022-2023/2gt1/T2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1643816d",
   "metadata": {},
   "source": [
    "# Etude des notes pour le 2e trimestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed2de9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/*\n",
       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
       " * @license MIT\n",
       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
       " */\n",
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       "/*!\n",
       " * The buffer module from node.js, for the browser.\n",
       " *\n",
       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
       " * @license  MIT\n",
       " */\n",
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       "/*!\n",
       " * Determine if an object is a Buffer\n",
       " *\n",
       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
       " * @license  MIT\n",
       " */\n",
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       "/*!\n",
       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
       " *\n",
       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
       " * Licensed under the MIT license.\n",
       " */\n",
       "\"use strict\";var n=t(\"repeat-string\");e.exports=function(t,e,r){return n(r=void 0!==r?r+\"\":\" \",e)+t}},{\"repeat-string\":277}],265:[function(t,e,r){e.exports=function(t,e){e||(e=[0,\"\"]),t=String(t);var r=parseFloat(t,10);return e[0]=r,e[1]=t.match(/[\\d.\\-\\+]*\\s*(.*)/)[1]||\"\",e}},{}],266:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=0|e.length,i=t.length,a=[new Array(r),new Array(r)],o=0;o<r;++o)a[0][o]=[],a[1][o]=[];for(o=0;o<i;++o){var s=t[o];a[0][s[0]].push(s),a[1][s[1]].push(s)}var l=[];for(o=0;o<r;++o)a[0][o].length+a[1][o].length===0&&l.push([o]);function c(t,e){var r=a[e][t[e]];r.splice(r.indexOf(t),1)}function u(t,r,i){for(var o,s,l,u=0;u<2;++u)if(a[u][r].length>0){o=a[u][r][0],l=u;break}s=o[1^l];for(var f=0;f<2;++f)for(var h=a[f][r],p=0;p<h.length;++p){var d=h[p],m=d[1^f];n(e[t],e[r],e[s],e[m])>0&&(o=d,s=m,l=f)}return i||o&&c(o,l),s}function f(t,r){var i=a[r][t][0],o=[t];c(i,r);for(var s=i[1^r];;){for(;s!==t;)o.push(s),s=u(o[o.length-2],s,!1);if(a[0][t].length+a[1][t].length===0)break;var l=o[o.length-1],f=t,h=o[1],p=u(l,f,!0);if(n(e[l],e[f],e[h],e[p])<0)break;o.push(t),s=u(l,f)}return o}function h(t,e){return e[1]===e[e.length-1]}for(o=0;o<r;++o)for(var p=0;p<2;++p){for(var d=[];a[p][o].length>0;){a[0][o].length;var m=f(o,p);h(0,m)?d.push.apply(d,m):(d.length>0&&l.push(d),d=m)}d.length>0&&l.push(d)}return l};var n=t(\"compare-angle\")},{\"compare-angle\":54}],267:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=n(t,e.length),i=new Array(e.length),a=new Array(e.length),o=[],s=0;s<e.length;++s){var l=r[s].length;a[s]=l,i[s]=!0,l<=1&&o.push(s)}for(;o.length>0;){var c=o.pop();i[c]=!1;var u=r[c];for(s=0;s<u.length;++s){var f=u[s];0==--a[f]&&o.push(f)}}var h=new Array(e.length),p=[];for(s=0;s<e.length;++s)if(i[s]){c=p.length;h[s]=c,p.push(e[s])}else h[s]=-1;var d=[];for(s=0;s<t.length;++s){var m=t[s];i[m[0]]&&i[m[1]]&&d.push([h[m[0]],h[m[1]]])}return[d,p]};var n=t(\"edges-to-adjacency-list\")},{\"edges-to-adjacency-list\":66}],268:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=c(t,e);t=r[0];for(var f=(e=r[1]).length,h=(t.length,n(t,e.length)),p=0;p<f;++p)if(h[p].length%2==1)throw new Error(\"planar-graph-to-polyline: graph must be manifold\");var d=i(t,e);var m=(d=d.filter((function(t){for(var r=t.length,n=[0],i=0;i<r;++i){var a=e[t[i]],l=e[t[(i+1)%r]],c=o(-a[0],a[1]),u=o(-a[0],l[1]),f=o(l[0],a[1]),h=o(l[0],l[1]);n=s(n,s(s(c,u),s(f,h)))}return n[n.length-1]>0}))).length,g=new Array(m),v=new Array(m);for(p=0;p<m;++p){g[p]=p;var y=new Array(m),x=d[p].map((function(t){return e[t]})),b=a([x]),_=0;t:for(var w=0;w<m;++w)if(y[w]=0,p!==w){for(var T=(H=d[w]).length,k=0;k<T;++k){var A=b(e[H[k]]);if(0!==A){A<0&&(y[w]=1,_+=1);continue t}}y[w]=1,_+=1}v[p]=[_,p,y]}v.sort((function(t,e){return e[0]-t[0]}));for(p=0;p<m;++p){var M=(y=v[p])[1],S=y[2];for(w=0;w<m;++w)S[w]&&(g[w]=M)}var E=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=[];return e}(m);for(p=0;p<m;++p)E[p].push(g[p]),E[g[p]].push(p);var L={},C=u(f,!1);for(p=0;p<m;++p)for(T=(H=d[p]).length,w=0;w<T;++w){var P=H[w],I=H[(w+1)%T],O=Math.min(P,I)+\":\"+Math.max(P,I);if(O in L){var z=L[O];E[z].push(p),E[p].push(z),C[P]=C[I]=!0}else L[O]=p}function D(t){for(var e=t.length,r=0;r<e;++r)if(!C[t[r]])return!1;return!0}var R=[],F=u(m,-1);for(p=0;p<m;++p)g[p]!==p||D(d[p])?F[p]=-1:(R.push(p),F[p]=0);r=[];for(;R.length>0;){var B=R.pop(),N=E[B];l(N,(function(t,e){return t-e}));var j,U=N.length,V=F[B];if(0===V){var H=d[B];j=[H]}for(p=0;p<U;++p){var q=N[p];if(!(F[q]>=0))if(F[q]=1^V,R.push(q),0===V)D(H=d[q])||(H.reverse(),j.push(H))}0===V&&r.push(j)}return r};var n=t(\"edges-to-adjacency-list\"),i=t(\"planar-dual\"),a=t(\"point-in-big-polygon\"),o=t(\"two-product\"),s=t(\"robust-sum\"),l=t(\"uniq\"),c=t(\"./lib/trim-leaves\");function u(t,e){for(var r=new Array(t),n=0;n<t;++n)r[n]=e;return r}},{\"./lib/trim-leaves\":267,\"edges-to-adjacency-list\":66,\"planar-dual\":266,\"point-in-big-polygon\":269,\"robust-sum\":289,\"two-product\":306,uniq:310}],269:[function(t,e,r){e.exports=function(t){for(var e=t.length,r=[]
       "/*!\n",
       " * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
       " *\n",
       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
       " * Licensed under the MIT License.\n",
       " */\n",
       "\"use strict\";var n,i=\"\";e.exports=function(t,e){if(\"string\"!=typeof t)throw new TypeError(\"expected a string\");if(1===e)return t;if(2===e)return t+t;var r=t.length*e;if(n!==t||void 0===n)n=t,i=\"\";else if(i.length>=r)return i.substr(0,r);for(;r>i.length&&e>1;)1&e&&(i+=t),e>>=1,t+=t;return i=(i+=t).substr(0,r)}},{}],278:[function(t,e,r){(function(t){(function(){e.exports=t.performance&&t.performance.now?function(){return performance.now()}:Date.now||function(){return+new Date}}).call(this)}).call(this,void 0!==n?n:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{}],279:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.length,r=t[t.length-1],n=e,i=e-2;i>=0;--i){var a=r,o=t[i];(l=o-((r=a+o)-a))&&(t[--n]=r,r=l)}var s=0;for(i=n;i<e;++i){var l;a=t[i];(l=(o=r)-((r=a+o)-a))&&(t[s++]=l)}return t[s++]=r,t.length=s,t}},{}],280:[function(t,e,r){\"use strict\";var n=t(\"two-product\"),i=t(\"robust-sum\"),a=t(\"robust-scale\"),o=t(\"robust-compress\");function s(t,e,r,n){return function(e){return n(t(r(e[0][0],e[1][1]),r(-e[0][1],e[1][0])))}}function l(t,e,r,n){return function(i){return n(t(e(t(r(i[1][1],i[2][2]),r(-i[1][2],i[2][1])),i[0][0]),t(e(t(r(i[1][0],i[2][2]),r(-i[1][2],i[2][0])),-i[0][1]),e(t(r(i[1][0],i[2][1]),r(-i[1][1],i[2][0])),i[0][2]))))}}function c(t){return(2===t?s:3===t?l:void 0)(i,a,n,o)}var u=[function(){return[0]},function(t){return[t[0][0]]}];function f(t,e,r,n,i,a){return function(o){switch(o.length){case 0:return t(o);case 1:return e(o);case 2:return r(o);case 3:return n(o)}var s=i[o.length];return s||(s=i[o.length]=a(o.length)),s(o)}}!function(){for(;u.length<4;)u.push(c(u.length));e.exports=f.apply(void 0,u.concat([u,c]));for(var t=0;t<u.length;++t)e.exports[t]=u[t]}()},{\"robust-compress\":279,\"robust-scale\":286,\"robust-sum\":289,\"two-product\":306}],281:[function(t,e,r){\"use strict\";var n=t(\"two-product\"),i=t(\"robust-sum\");e.exports=function(t,e){for(var r=n(t[0],e[0]),a=1;a<t.length;++a)r=i(r,n(t[a],e[a]));return r}},{\"robust-sum\":289,\"two-product\":306}],282:[function(t,e,r){\"use strict\";var n=t(\"two-product\"),i=t(\"robust-sum\"),a=t(\"robust-subtract\"),o=t(\"robust-scale\");function s(t){return(3===t?l:4===t?c:5===t?u:f)(i,a,n,o)}function l(t,e,r,n){return function(i,a,o){var s=r(i[0],i[0]),l=n(s,a[0]),c=n(s,o[0]),u=r(a[0],a[0]),f=n(u,i[0]),h=n(u,o[0]),p=r(o[0],o[0]),d=n(p,i[0]),m=n(p,a[0]),g=t(e(m,h),e(f,l)),v=e(d,c),y=e(g,v);return y[y.length-1]}}function c(t,e,r,n){return function(i,a,o,s){var l=t(r(i[0],i[0]),r(i[1],i[1])),c=n(l,a[0]),u=n(l,o[0]),f=n(l,s[0]),h=t(r(a[0],a[0]),r(a[1],a[1])),p=n(h,i[0]),d=n(h,o[0]),m=n(h,s[0]),g=t(r(o[0],o[0]),r(o[1],o[1])),v=n(g,i[0]),y=n(g,a[0]),x=n(g,s[0]),b=t(r(s[0],s[0]),r(s[1],s[1])),_=n(b,i[0]),w=n(b,a[0]),T=n(b,o[0]),k=t(t(n(e(T,x),a[1]),t(n(e(w,m),-o[1]),n(e(y,d),s[1]))),t(n(e(w,m),i[1]),t(n(e(_,f),-a[1]),n(e(p,c),s[1])))),A=t(t(n(e(T,x),i[1]),t(n(e(_,f),-o[1]),n(e(v,u),s[1]))),t(n(e(y,d),i[1]),t(n(e(v,u),-a[1]),n(e(p,c),o[1])))),M=e(k,A);return M[M.length-1]}}function u(t,e,r,n){return function(i,a,o,s,l){var c=t(r(i[0],i[0]),t(r(i[1],i[1]),r(i[2],i[2]))),u=n(c,a[0]),f=n(c,o[0]),h=n(c,s[0]),p=n(c,l[0]),d=t(r(a[0],a[0]),t(r(a[1],a[1]),r(a[2],a[2]))),m=n(d,i[0]),g=n(d,o[0]),v=n(d,s[0]),y=n(d,l[0]),x=t(r(o[0],o[0]),t(r(o[1],o[1]),r(o[2],o[2]))),b=n(x,i[0]),_=n(x,a[0]),w=n(x,s[0]),T=n(x,l[0]),k=t(r(s[0],s[0]),t(r(s[1],s[1]),r(s[2],s[2]))),A=n(k,i[0]),M=n(k,a[0]),S=n(k,o[0]),E=n(k,l[0]),L=t(r(l[0],l[0]),t(r(l[1],l[1]),r(l[2],l[2]))),C=n(L,i[0]),P=n(L,a[0]),I=n(L,o[0]),O=n(L,s[0]),z=t(t(t(n(t(n(e(O,E),o[1]),t(n(e(I,T),-s[1]),n(e(S,w),l[1]))),a[2]),t(n(t(n(e(O,E),a[1]),t(n(e(P,y),-s[1]),n(e(M,v),l[1]))),-o[2]),n(t(n(e(I,T),a[1]),t(n(e(P,y),-o[1]),n(e(_,g),l[1]))),s[2]))),t(n(t(n(e(S,w),a[1]),t(n(e(M,v),-o[1]),n(e(_,g),s[1]))),-l[2]),t(n(t(n(e(O,E),a[1]),t(n(e(P,y),-s[1]),n(e(M,v),l[1]))),i[2]),n(t(n(e(O,E),i[1]),t(n(e(C,p),-s[1]),n(e(A,h),l[1]))),-a[2])))),t(t(n(t(n(e(P,y),i[1]),t(n(e(C,p),-a[1]),n(e(m,u),l[1]))),s[2]),t(n(t(n(e(M,v),i[1]),t(n(e(A,h),-a[1]),n(e(m,u),s[1]))),-l[2]),n(t(n(e(S,
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown as md\n",
    "from IPython.display import display, HTML\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from pathlib import Path\n",
    "from datetime import datetime\n",
    "from recopytex import flat_df_students, pp_q_scores\n",
    "from datetime import datetime\n",
    "from pathlib import Path\n",
    "\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objects as go\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.express as px\n",
    "\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "init_notebook_mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "556ebf8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('230317_DS5.csv'),\n",
       " PosixPath('220922_DS1.csv'),\n",
       " PosixPath('230224_DS4.csv'),\n",
       " PosixPath('221201_DM1.csv'),\n",
       " PosixPath('230118_DS3.csv'),\n",
       " PosixPath('221214_DS2.csv')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_files = list(Path(\"./\").glob(\"**/*.csv\"))\n",
    "score_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "304bd86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "reject = [\"221214_DS2.csv\", \"230317_DS5.csv\"]\n",
    "dfs = []\n",
    "for file in score_files:\n",
    "    if file.name not in reject:\n",
    "        score_df = pd.read_csv(file, encoding=\"UTF8\")\n",
    "        flat = flat_df_students(score_df).dropna(subset=[\"Score\"])\n",
    "        pp_q = pp_q_scores(flat)\n",
    "        dfs.append(pp_q)\n",
    "df = pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4788fc47",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype(dtype={\n",
    "    \"Nom\": \"str\",\n",
    "    \"Trimestre\": \"int64\",\n",
    "    \"Exercice\": \"str\",\n",
    "    \"Question\": \"str\",\n",
    "    \"Competence\": \"str\",\n",
    "    \"Domaine\": \"str\",\n",
    "    \"Commentaire\": \"str\",\n",
    "    \"Est_nivele\": \"bool\",\n",
    "    \"Bareme\": \"float64\",\n",
    "    \"Eleve\": \"str\",\n",
    "    \"Score\": \"float64\",\n",
    "    \n",
    "})\n",
    "df.Date = df.Date.apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "786f5b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_df = df[df[\"Trimestre\"] == 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7ea54c",
   "metadata": {},
   "source": [
    "## Fonctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "92e292b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_pt(df, diff_col, eleve_col,  score_col, aggfunc):\n",
    "    \"\"\" Fait une table pivot de df\n",
    "    \n",
    "    Préparation des données pour les clustering\n",
    "    \"\"\"\n",
    "    pt = pd.pivot_table(df,\n",
    "          index=[eleve_col],\n",
    "          columns=[diff_col],\n",
    "          values=[score_col],\n",
    "          aggfunc=aggfunc,)\n",
    "    pt.dropna(inplace=True)\n",
    "    cols_names = pt.columns.get_level_values(-1)\n",
    "    pt.columns = cols_names\n",
    "    return pt.reset_index(), cols_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "557297b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clustering(pt, cols_names, method, method_arg={}):\n",
    "    \"\"\" Applique la méthode de clustering sur pt\"\"\"\n",
    "    clu = method(**method_arg)\n",
    "    clu.fit(pt[cols_names])\n",
    "    return pd.Series(clu.labels_, index=pt.index), clu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "356d4e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_polar_cluster(pt, clustername, clustering, fields):\n",
    "    groups = pt.groupby([clustername]).agg({\"Eleve\":list})\n",
    "    for i, cluster in enumerate(clustering.cluster_centers_):\n",
    "        fig = go.Figure()\n",
    "        fig.add_trace(go.Scatterpolar(\n",
    "            r = cluster,\n",
    "            theta = fields,\n",
    "            name = i,\n",
    "            fill=\"toself\"\n",
    "        ))\n",
    "    \n",
    "\n",
    "        for eleve in pt[pt[clustername]==i].iterrows():\n",
    "           fig.add_trace(go.Scatterpolar(\n",
    "                r = eleve[1][fields],\n",
    "                theta = fields,\n",
    "                name=eleve[1][\"Eleve\"],       \n",
    "           ))\n",
    "        fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84048230",
   "metadata": {},
   "source": [
    "## Classification des élèves\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52f10a04",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18ababdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_df = T2_df.assign(\n",
    "    q_id = lambda x: x['Nom']+ x['Exercice'] + x['Question'] + x['Commentaire']\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9ca48e",
   "metadata": {},
   "source": [
    "## Sur les domaines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "548fcb76",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_pt_dom, domains = build_pt(T2_df, \n",
    "                             diff_col=\"Domaine\", \n",
    "                             eleve_col=\"Eleve\", \n",
    "                             score_col=\"Normalise\", \n",
    "                             aggfunc=[np.mean])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e81e7689",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_pt_dom[\"affinity_cluster\"], affinity_cluster = clustering(T2_pt_dom, domains, AffinityPropagation)\n",
    "T2_pt_dom[\"kmeans5_cluster\"], kmeans5_cluster = clustering(T2_pt_dom, domains, KMeans, {'n_clusters':5, 'n_init':\"auto\"})\n",
    "dom_clusters = {\n",
    "    \"affinity_cluster\": affinity_cluster,\n",
    "    \"kmeans5_cluster\": kmeans5_cluster,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "56365bca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "fill": "toself",
         "name": "0",
         "r": [
          0.7404938271604938,
          0.5604745370370371,
          0.8644444444444445,
          0.5182222222222223,
          0.7319444444444445,
          0.2815555555555556,
          0.37
         ],
         "theta": [
          "Calcul littéral",
          "Fonctions",
          "Fractions",
          "Géométrie",
          "Information chiffrée",
          "Probabilité",
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   ],
   "source": [
    "plot_polar_cluster(T2_pt_dom, \"kmeans5_cluster\", dom_clusters[\"kmeans5_cluster\"], domains)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadbe55d",
   "metadata": {},
   "source": [
    "### Sur les compétences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "43e97480",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_pt_comp, domains = build_pt(T2_df, \n",
    "                             diff_col=\"Competence\", \n",
    "                             eleve_col=\"Eleve\", \n",
    "                             score_col=\"Normalise\", \n",
    "                             aggfunc=[np.mean])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9b2938a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "T2_pt_comp[\"affinity_cluster\"], affinity_cluster = clustering(T2_pt_comp, domains, AffinityPropagation)\n",
    "T2_pt_comp[\"kmeans5_cluster\"], kmeans5_cluster = clustering(T2_pt_comp, domains, KMeans, {'n_clusters':5, 'n_init':\"auto\"})\n",
    "comp_clusters = {\n",
    "    \"affinity_cluster\": affinity_cluster,\n",
    "    \"kmeans5_cluster\": kmeans5_cluster,\n",
    "}"
   ]
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  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "56c0349a",
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   ],
   "source": [
    "plot_polar_cluster(T2_pt_comp, \"kmeans5_cluster\", comp_clusters[\"kmeans5_cluster\"], competences)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b12d58a",
   "metadata": {},
   "source": [
    "### Graphique en araignées pour les groupes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43d29f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia = []\n",
    "for k in range(2, 20):\n",
    "    clu = KMeans(n_clusters=k, n_init=\"auto\")\n",
    "    clu.fit(T2_pt_comp[competences])\n",
    "    inertia.append(clu.inertia_)\n",
    "print(inertia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4933d43a",
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia_df = pd.DataFrame({'index': range(2, 20), 'inertia':inertia})\n",
    "px.line(inertia_df, x='index', y='inertia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "865ae7fe",
   "metadata": {},
   "outputs": [],
   "source": []
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