diff --git a/2gt1/T2.ipynb b/2gt1/T2.ipynb
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--- /dev/null
+++ b/2gt1/T2.ipynb
@@ -0,0 +1,10819 @@
+{
+ "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": {
+ "text/html": [
+ " \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
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+ "### Sur les compétences"
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+ "T2_pt_comp, domains = build_pt(T2_df, \n",
+ " diff_col=\"Competence\", \n",
+ " eleve_col=\"Eleve\", \n",
+ " score_col=\"Normalise\", \n",
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+ "T2_pt_comp[\"kmeans5_cluster\"], kmeans5_cluster = clustering(T2_pt_comp, domains, KMeans, {'n_clusters':5, 'n_init':\"auto\"})\n",
+ "comp_clusters = {\n",
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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)"
+ ]
+ },
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+ "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')"
+ ]
+ },
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+ "cell_type": "code",
+ "execution_count": null,
+ "id": "865ae7fe",
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