{
"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": [
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"source": [
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"### Sur les compétences"
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},
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"T2_pt_comp, domains = build_pt(T2_df, \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",
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"}"
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0.2222222222222222,
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0.3333333333333333,
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0.4444444444444444,
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[
0.5555555555555556,
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0.6666666666666666,
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0.7777777777777778,
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[
0.8888888888888888,
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1,
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"colorway": [
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"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
}
}
},
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}