From 84eecee5dafb07d8677b5be84a7f9bf6df42006e Mon Sep 17 00:00:00 2001 From: Bertrand Benjamin Date: Mon, 20 Mar 2023 17:46:24 +0100 Subject: [PATCH] Feat(2nd): clean T2 --- 2gt1/T2.ipynb | 10819 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 10819 insertions(+) create mode 100644 2gt1/T2.ipynb diff --git a/2gt1/T2.ipynb b/2gt1/T2.ipynb new file mode 100644 index 0000000..5e24543 --- /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 + }, + "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, + 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