{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# 2A.ml - Statistiques descriptives avec scikit-learn - correction\n", "\n", "ACP, CAH, r\u00e9gression lin\u00e9aire, correction."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/html": ["
\n", ""], "text/plain": [""]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": ["# R\u00e9pare une incompatibilit\u00e9 entre scipy 1.0 et statsmodels 0.8.\n", "from pymyinstall.fix import fix_scipy10_for_statsmodels08\n", "fix_scipy10_for_statsmodels08()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Pr\u00e9requis de l'\u00e9nonc\u00e9"]}, {"cell_type": "code", "execution_count": 5, "metadata": {"scrolled": false}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["convert dbase into sqllite\n", "unable to execute a SQL request (1)(file salaries2011.db3)\n", "create table varlist (VARIABLE TEXT, LIBELLE TEXT, TYPE TEXT, LONGUEUR REAL)\n", "La base de donn\u00e9es est d\u00e9j\u00e0 renseign\u00e9e.\n"]}], "source": ["import pyensae.datasource\n", "f = pyensae.datasource.download_data(\"dads2011_gf_salaries11_dbase.zip\",\n", " website=\"https://www.insee.fr/fr/statistiques/fichier/2011542/\")\n", "\n", "import pandas\n", "try:\n", " from dbfread import DBF\n", " use_dbfread = True\n", "except ImportError as e :\n", " use_dbfread = False\n", " \n", "if use_dbfread:\n", " import os\n", " from pyensae.sql.database_exception import ExceptionSQL\n", " from pyensae.datasource import dBase2sqllite\n", " print(\"convert dbase into sqllite\")\n", " try:\n", " dBase2sqllite(\"salaries2011.db3\", \"varlist_salaries11.dbf\", overwrite_table=\"varlist\")\n", " dBase2sqllite(\"salaries2011.db3\", \"varmod_salaries11.dbf\", overwrite_table=\"varmod\")\n", " dBase2sqllite(\"salaries2011.db3\", 'salaries11.dbf', overwrite_table=\"salaries\", fLOG = print)\n", " except ExceptionSQL:\n", " print(\"La base de donn\u00e9es est d\u00e9j\u00e0 renseign\u00e9e.\")\n", "else :\n", " print(\"use of zipped version\")\n", " import pyensae.datasource\n", " db3 = pyensae.datasource.download_data(\"salaries2011.zip\")\n", " # pour aller plus vite, donn\u00e9es \u00e0 t\u00e9l\u00e9charger au\n", " # http://www.xavierdupre.fr/enseignement/complements/salaries2011.zip"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Exercice 1 : CAH (classification ascendante hi\u00e9rarchique)\n", "\n", "Le point commun de ces m\u00e9thodes est qu'elles ne sont pas supervis\u00e9es. L'objectif est de r\u00e9duire la complexit\u00e9 des donn\u00e9es. R\u00e9duire le nombre de dimensions pour l'ACP ou segmenter les observations pour les k-means et la CAH."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["(27, 21)\n"]}, {"data": {"text/html": ["\n", "\n", "
\n", " \n", " \n", " \n", " 1993 \n", " 1994 \n", " 1995 \n", " 1996 \n", " 1997 \n", " 1998 \n", " 1999 \n", " 2000 \n", " 2001 \n", " 2002 \n", " ... \n", " 2004 \n", " 2005 \n", " 2006 \n", " 2007 \n", " 2008 \n", " 2009 \n", " 2010 \n", " 2011 \n", " 2012 \n", " 2013 \n", " \n", " \n", " acad\u00e9mie \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Aix-Marseille \n", " 241357 \n", " 242298 \n", " 242096 \n", " 242295 \n", " 243660 \n", " 244608 \n", " 245536 \n", " 247288 \n", " 249331 \n", " 250871 \n", " ... \n", " 250622 \n", " 248208 \n", " 245755 \n", " 243832 \n", " 242309 \n", " 240664 \n", " 240432 \n", " 241336 \n", " 239051 \n", " 240115 \n", " \n", " \n", " Amiens \n", " 198281 \n", " 196871 \n", " 195709 \n", " 194055 \n", " 192893 \n", " 191862 \n", " 189636 \n", " 185977 \n", " 183357 \n", " 180973 \n", " ... \n", " 175610 \n", " 172110 \n", " 168718 \n", " 165295 \n", " 163116 \n", " 162548 \n", " 163270 \n", " 164422 \n", " 165275 \n", " 166345 \n", " \n", " \n", " Besan\u00e7on \n", " 116373 \n", " 115600 \n", " 114282 \n", " 113312 \n", " 112076 \n", " 110261 \n", " 108106 \n", " 105463 \n", " 103336 \n", " 102264 \n", " ... \n", " 100117 \n", " 98611 \n", " 97038 \n", " 95779 \n", " 95074 \n", " 94501 \n", " 94599 \n", " 94745 \n", " 94351 \n", " 94613 \n", " \n", " \n", " Bordeaux \n", " 253551 \n", " 252644 \n", " 249658 \n", " 247708 \n", " 247499 \n", " 245757 \n", " 244992 \n", " 243047 \n", " 243592 \n", " 245198 \n", " ... \n", " 244805 \n", " 244343 \n", " 242602 \n", " 242933 \n", " 243146 \n", " 244336 \n", " 246806 \n", " 250626 \n", " 252085 \n", " 255761 \n", " \n", " \n", " Caen \n", " 145435 \n", " 144369 \n", " 141883 \n", " 140658 \n", " 139585 \n", " 137704 \n", " 135613 \n", " 133255 \n", " 131206 \n", " 129271 \n", " ... \n", " 125552 \n", " 123889 \n", " 122550 \n", " 121002 \n", " 119857 \n", " 119426 \n", " 119184 \n", " 119764 \n", " 119010 \n", " 119238 \n", " \n", " \n", "
\n", "
5 rows \u00d7 21 columns
\n", "
"], "text/plain": [" 1993 1994 1995 1996 1997 1998 1999 2000 \\\n", "acad\u00e9mie \n", "Aix-Marseille 241357 242298 242096 242295 243660 244608 245536 247288 \n", "Amiens 198281 196871 195709 194055 192893 191862 189636 185977 \n", "Besan\u00e7on 116373 115600 114282 113312 112076 110261 108106 105463 \n", "Bordeaux 253551 252644 249658 247708 247499 245757 244992 243047 \n", "Caen 145435 144369 141883 140658 139585 137704 135613 133255 \n", "\n", " 2001 2002 ... 2004 2005 2006 2007 2008 \\\n", "acad\u00e9mie ... \n", "Aix-Marseille 249331 250871 ... 250622 248208 245755 243832 242309 \n", "Amiens 183357 180973 ... 175610 172110 168718 165295 163116 \n", "Besan\u00e7on 103336 102264 ... 100117 98611 97038 95779 95074 \n", "Bordeaux 243592 245198 ... 244805 244343 242602 242933 243146 \n", "Caen 131206 129271 ... 125552 123889 122550 121002 119857 \n", "\n", " 2009 2010 2011 2012 2013 \n", "acad\u00e9mie \n", "Aix-Marseille 240664 240432 241336 239051 240115 \n", "Amiens 162548 163270 164422 165275 166345 \n", "Besan\u00e7on 94501 94599 94745 94351 94613 \n", "Bordeaux 244336 246806 250626 252085 255761 \n", "Caen 119426 119184 119764 119010 119238 \n", "\n", "[5 rows x 21 columns]"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["import pandas, numpy, matplotlib.pyplot as plt\n", "from sklearn.decomposition import PCA\n", "import pyensae.datasource\n", "\n", "pyensae.datasource.download_data(\"eleve_region.txt\")\n", "df = pandas.read_csv(\"eleve_region.txt\", sep=\"\\t\", encoding=\"utf8\", index_col=0)\n", "print(df.shape)\n", "df.head(n=5)"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", "\n", "
\n", " \n", " \n", " \n", " 1993 \n", " 1994 \n", " 1995 \n", " 1996 \n", " 1997 \n", " 1998 \n", " 1999 \n", " 2000 \n", " 2001 \n", " 2002 \n", " ... \n", " 2004 \n", " 2005 \n", " 2006 \n", " 2007 \n", " 2008 \n", " 2009 \n", " 2010 \n", " 2011 \n", " 2012 \n", " 2013 \n", " \n", " \n", " acad\u00e9mie \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Aix-Marseille \n", " 1.0 \n", " 1.003899 \n", " 1.003062 \n", " 1.003886 \n", " 1.009542 \n", " 1.013470 \n", " 1.017315 \n", " 1.024574 \n", " 1.033038 \n", " 1.039419 \n", " ... \n", " 1.038387 \n", " 1.028385 \n", " 1.018222 \n", " 1.010255 \n", " 1.003944 \n", " 0.997129 \n", " 0.996168 \n", " 0.999913 \n", " 0.990446 \n", " 0.994854 \n", " \n", " \n", " Amiens \n", " 1.0 \n", " 0.992889 \n", " 0.987029 \n", " 0.978687 \n", " 0.972826 \n", " 0.967627 \n", " 0.956400 \n", " 0.937947 \n", " 0.924733 \n", " 0.912710 \n", " ... \n", " 0.885662 \n", " 0.868011 \n", " 0.850904 \n", " 0.833640 \n", " 0.822651 \n", " 0.819786 \n", " 0.823427 \n", " 0.829237 \n", " 0.833539 \n", " 0.838936 \n", " \n", " \n", " Besan\u00e7on \n", " 1.0 \n", " 0.993358 \n", " 0.982032 \n", " 0.973697 \n", " 0.963076 \n", " 0.947479 \n", " 0.928961 \n", " 0.906250 \n", " 0.887972 \n", " 0.878761 \n", " ... \n", " 0.860311 \n", " 0.847370 \n", " 0.833853 \n", " 0.823035 \n", " 0.816976 \n", " 0.812053 \n", " 0.812895 \n", " 0.814149 \n", " 0.810764 \n", " 0.813015 \n", " \n", " \n", " Bordeaux \n", " 1.0 \n", " 0.996423 \n", " 0.984646 \n", " 0.976955 \n", " 0.976131 \n", " 0.969261 \n", " 0.966243 \n", " 0.958572 \n", " 0.960722 \n", " 0.967056 \n", " ... \n", " 0.965506 \n", " 0.963684 \n", " 0.956817 \n", " 0.958123 \n", " 0.958963 \n", " 0.963656 \n", " 0.973398 \n", " 0.988464 \n", " 0.994218 \n", " 1.008716 \n", " \n", " \n", " Caen \n", " 1.0 \n", " 0.992670 \n", " 0.975577 \n", " 0.967154 \n", " 0.959776 \n", " 0.946842 \n", " 0.932465 \n", " 0.916251 \n", " 0.902162 \n", " 0.888858 \n", " ... \n", " 0.863286 \n", " 0.851851 \n", " 0.842644 \n", " 0.832001 \n", " 0.824128 \n", " 0.821164 \n", " 0.819500 \n", " 0.823488 \n", " 0.818304 \n", " 0.819871 \n", " \n", " \n", "
\n", "
5 rows \u00d7 21 columns
\n", "
"], "text/plain": [" 1993 1994 1995 1996 1997 1998 \\\n", "acad\u00e9mie \n", "Aix-Marseille 1.0 1.003899 1.003062 1.003886 1.009542 1.013470 \n", "Amiens 1.0 0.992889 0.987029 0.978687 0.972826 0.967627 \n", "Besan\u00e7on 1.0 0.993358 0.982032 0.973697 0.963076 0.947479 \n", "Bordeaux 1.0 0.996423 0.984646 0.976955 0.976131 0.969261 \n", "Caen 1.0 0.992670 0.975577 0.967154 0.959776 0.946842 \n", "\n", " 1999 2000 2001 2002 ... 2004 \\\n", "acad\u00e9mie ... \n", "Aix-Marseille 1.017315 1.024574 1.033038 1.039419 ... 1.038387 \n", "Amiens 0.956400 0.937947 0.924733 0.912710 ... 0.885662 \n", "Besan\u00e7on 0.928961 0.906250 0.887972 0.878761 ... 0.860311 \n", "Bordeaux 0.966243 0.958572 0.960722 0.967056 ... 0.965506 \n", "Caen 0.932465 0.916251 0.902162 0.888858 ... 0.863286 \n", "\n", " 2005 2006 2007 2008 2009 2010 \\\n", "acad\u00e9mie \n", "Aix-Marseille 1.028385 1.018222 1.010255 1.003944 0.997129 0.996168 \n", "Amiens 0.868011 0.850904 0.833640 0.822651 0.819786 0.823427 \n", "Besan\u00e7on 0.847370 0.833853 0.823035 0.816976 0.812053 0.812895 \n", "Bordeaux 0.963684 0.956817 0.958123 0.958963 0.963656 0.973398 \n", "Caen 0.851851 0.842644 0.832001 0.824128 0.821164 0.819500 \n", "\n", " 2011 2012 2013 \n", "acad\u00e9mie \n", "Aix-Marseille 0.999913 0.990446 0.994854 \n", "Amiens 0.829237 0.833539 0.838936 \n", "Besan\u00e7on 0.814149 0.810764 0.813015 \n", "Bordeaux 0.988464 0.994218 1.008716 \n", "Caen 0.823488 0.818304 0.819871 \n", "\n", "[5 rows x 21 columns]"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["for c in df.columns:\n", " if c != \"1993\":\n", " df[c] /= df [\"1993\"]\n", "df[\"1993\"] /= df[\"1993\"]\n", "df.head()"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/plain": ["AgglomerativeClustering(compute_full_tree=True)"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["from sklearn.cluster import AgglomerativeClustering\n", "\n", "ward = AgglomerativeClustering(linkage='ward', compute_full_tree=True).fit(df)\n", "ward"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["from scipy.cluster.hierarchy import dendrogram\n", "import matplotlib.pyplot as plt\n", "\n", "dendro = [ ]\n", "for a,b in ward.children_:\n", " dendro.append([a, b, float(len(dendro)+1), len(dendro)+1])\n", " # le dernier coefficient devrait contenir le nombre de feuilles d\u00e9pendant de ce noeud\n", " # et non le dernier indice\n", " # de m\u00eame, le niveau (3\u00e8me colonne) ne devrait pas \u00eatre le nombre de noeud\n", " # mais la distance de Ward\n", " \n", "fig = plt.figure( figsize=(8,8) )\n", "ax = fig.add_subplot(1,1,1) \n", "r = dendrogram(dendro, color_threshold=1, labels=list(df.index),\n", " show_leaf_counts=True, ax=ax, orientation=\"left\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Je reprends \u00e9galement le graphique montrant la matrice de corr\u00e9lations qu'on peut \u00e9galement obtenir avec [seaborn : clustermap](https://seaborn.pydata.org/examples/structured_heatmap.html)."]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["from scipy.spatial.distance import pdist, squareform\n", "\n", "data_dist = pdist(df)\n", "\n", "fig = plt.figure(figsize=(8,8))\n", "\n", "# x ywidth height\n", "ax1 = fig.add_axes([0.05,0.1,0.2,0.6])\n", "Z1 = dendrogram(dendro, orientation='right',labels=list(df.index))\n", "ax1.set_xticks([])\n", "\n", "# Compute and plot second dendrogram.\n", "ax2 = fig.add_axes([0.3,0.71,0.6,0.2])\n", "Z2 = dendrogram(dendro)\n", "ax2.set_xticks([])\n", "ax2.set_yticks([])\n", "\n", "# Compute and plot the heatmap\n", "axmatrix = fig.add_axes([0.3,0.1,0.6,0.6])\n", "idx1 = Z1['leaves']\n", "idx2 = Z2['leaves']\n", "\n", "D = squareform(data_dist)\n", "D = D[idx1,:]\n", "D = D[:,idx2]\n", "im = axmatrix.matshow(D, aspect='auto', origin='lower', cmap=plt.cm.YlGnBu)\n", "axmatrix.set_xticks([])\n", "axmatrix.set_yticks([])\n", "\n", "# Plot colorbar.\n", "axcolor = fig.add_axes([0.91,0.1,0.02,0.6])\n", "plt.colorbar(im, cax=axcolor)\n", "plt.title(\"Matrice de corr\u00e9lation et dendogramme.\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Exercice 2 : r\u00e9gression lin\u00e9aire\n", "\n", "Ce sont trois m\u00e9thodes supervis\u00e9es : on s'en sert pour expliquer pr\u00e9dire le lien entre deux variables $X$ et $Y$ (ou ensemble de variables) ou pr\u00e9dire $Y$ en fonction de $X$. On suppose que les donn\u00e9es ont d\u00e9j\u00e0 \u00e9t\u00e9 t\u00e9l\u00e9charg\u00e9es (voir l'\u00e9nonc\u00e9 de cet exercice). On continue avec l'extraction de l'\u00e2ge, du sexe et du salaire comme indiqu\u00e9 dans l'\u00e9nonc\u00e9."]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", "\n", "
\n", " \n", " \n", " \n", " VARIABLE \n", " MODALITE \n", " MODLIBELLE \n", " montant \n", " \n", " \n", " \n", " \n", " 8957 \n", " TRNNETO \n", " 00 \n", " [0 ; 200[ euros \n", " 100.0 \n", " \n", " \n", " 8958 \n", " TRNNETO \n", " 01 \n", " [200 ; 500[ euros \n", " 350.0 \n", " \n", " \n", " 8959 \n", " TRNNETO \n", " 02 \n", " [500 ; 1 000[ euros \n", " 750.0 \n", " \n", " \n", " 8960 \n", " TRNNETO \n", " 03 \n", " [1 000 ; 1 500[ euros \n", " 1250.0 \n", " \n", " \n", " 8961 \n", " TRNNETO \n", " 04 \n", " [1\u00a0500 ; 2 000[ euros \n", " 1750.0 \n", " \n", " \n", "
\n", "
"], "text/plain": [" VARIABLE MODALITE MODLIBELLE montant\n", "8957 TRNNETO 00 [0 ; 200[ euros 100.0\n", "8958 TRNNETO 01 [200 ; 500[ euros 350.0\n", "8959 TRNNETO 02 [500 ; 1 000[ euros 750.0\n", "8960 TRNNETO 03 [1 000 ; 1 500[ euros 1250.0\n", "8961 TRNNETO 04 [1\u00a0500 ; 2 000[ euros 1750.0"]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["import sqlite3, pandas\n", "con = sqlite3.connect(\"salaries2011.db3\")\n", "df = pandas.io.sql.read_sql(\"select * from varmod\", con)\n", "con.close()\n", "\n", "values = df[ df.VARIABLE == \"TRNNETO\"].copy()\n", "\n", "def process_intervalle(s):\n", " # [14 000 ; 16 000[ euros\n", " acc = \"0123456789;+\"\n", " s0 = \"\".join(c for c in s if c in acc)\n", " spl = s0.split(';')\n", " if len(spl) != 2:\n", " raise ValueError(\"Unable to process '{0}'\".format(s0))\n", " try:\n", " a = float(spl[0])\n", " except Exception as e:\n", " raise ValueError(\"Cannot interpret '{0}' - {1}\".format(s, spl))\n", " b = float(spl[1]) if \"+\" not in spl[1] else None\n", " if b is None:\n", " return a\n", " return (a+b) / 2.0\n", "\n", "values[\"montant\"] = values.apply(lambda r : process_intervalle(r[\"MODLIBELLE\"]), axis=1)\n", "values.head()"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"text/html": ["\n", "\n", "
\n", " \n", " \n", " \n", " AGE \n", " M \n", " F \n", " montant \n", " \n", " \n", " \n", " \n", " 0 \n", " 49.0 \n", " 1 \n", " 0 \n", " 750.0 \n", " \n", " \n", " 1 \n", " 27.0 \n", " 1 \n", " 0 \n", " 750.0 \n", " \n", " \n", " 2 \n", " 22.0 \n", " 1 \n", " 0 \n", " 750.0 \n", " \n", " \n", " 3 \n", " 26.0 \n", " 1 \n", " 0 \n", " 750.0 \n", " \n", " \n", " 4 \n", " 29.0 \n", " 0 \n", " 1 \n", " 750.0 \n", " \n", " \n", "
\n", "
"], "text/plain": [" AGE M F montant\n", "0 49.0 1 0 750.0\n", "1 27.0 1 0 750.0\n", "2 22.0 1 0 750.0\n", "3 26.0 1 0 750.0\n", "4 29.0 0 1 750.0"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["import sqlite3, pandas\n", "con = sqlite3.connect(\"salaries2011.db3\")\n", "data = pandas.io.sql.read_sql(\"select TRNNETO,AGE,SEXE from salaries\", con)\n", "con.close()\n", "\n", "salaires = data.merge (values, left_on=\"TRNNETO\", right_on=\"MODALITE\" )\n", "salaires[\"M\"] = salaires.apply(lambda r: 1 if r[\"SEXE\"] == \"1\" else 0, axis=1)\n", "salaires[\"F\"] = salaires.apply(lambda r: 1 if r[\"SEXE\"] == \"2\" else 0, axis=1)\n", "data = salaires[[\"AGE\",\"M\",\"F\",\"montant\"]]\n", "data = data [data.M + data.F > 0]\n", "data.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On supprime les valeurs manquantes :"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"data": {"text/plain": ["(2240691, 4)"]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}], "source": ["nonull = data.dropna().copy()\n", "nonull.shape"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### version scikit-learn\n", "\n", "La [r\u00e9gression lin\u00e9raire](http://scikit-learn.org/stable/modules/linear_model.html) suit :"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/plain": ["(2240691, 2)"]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["nonull[[\"AGE\",\"M\"]].dropna().shape"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["C:\\Python395_x64\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n", " warnings.warn(\n"]}, {"data": {"text/plain": ["(array([ 310.98873096, 4710.02901965]),\n", " 4927.219006697269,\n", " 'R^2=',\n", " 0.13957345814666222)"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["from sklearn import linear_model\n", "clf = linear_model.LinearRegression()\n", "clf.fit (nonull[[\"AGE\",\"M\"]].values, nonull.montant.values)\n", "clf.coef_, clf.intercept_, \"R^2=\", clf.score(\n", " nonull[[\"AGE\",\"M\"]], nonull.montant)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On prend un \u00e9chantillon al\u00e9atoire :"]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/plain": ["(2278, 5)"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["import random\n", "val = nonull.copy()\n", "val[\"rnd\"] = val.apply(lambda r: random.randint(0, 1000), axis=1)\n", "ech = val[val[\"rnd\"] == 1]\n", "ech.shape"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On s\u00e9pare homme et femmes :"]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["C:\\Python395_x64\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n", " warnings.warn(\n", "C:\\Python395_x64\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n", " warnings.warn(\n"]}, {"data": {"image/png": 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\n", "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["homme = ech[ech.M == 1]\n", "femme = ech[ech.M == 0]\n", "\n", "predh = clf.predict(homme[[\"AGE\",\"M\"]])\n", "predf = clf.predict(femme[[\"AGE\",\"M\"]])\n", "\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(16,6))\n", "plt.plot(homme.AGE, homme.montant, \"r.\")\n", "plt.plot(femme.AGE + 0.2, femme.montant, \"b.\")\n", "plt.plot(homme.AGE, predh, \"ro-\", label=\"hommes\")\n", "plt.plot(femme.AGE, predf, \"bo-\", label=\"femmes\")\n", "plt.legend()\n", "plt.title(\"Montant moyen par \u00e2ge et genre\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### version statsmodels"]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:7: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", " from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n", "C:\\Python395_x64\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", " from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"]}, {"name": "stdout", "output_type": "stream", "text": ["coefficients AGE 310.988731\n", "M 4710.029020\n", "one 4927.219007\n", "dtype: float64\n"]}, {"data": {"text/html": ["\n", "OLS Regression Results \n", "\n", " Dep. Variable: montant R-squared: 0.140 \n", " \n", "\n", " Model: OLS Adj. R-squared: 0.140 \n", " \n", "\n", " Method: Least Squares F-statistic: 1.817e+05 \n", " \n", "\n", " Date: Sat, 12 Feb 2022 Prob (F-statistic): 0.00 \n", " \n", "\n", " Time: 18:57:08 Log-Likelihood: -2.4023e+07 \n", " \n", "\n", " No. Observations: 2240691 AIC: 4.805e+07 \n", " \n", "\n", " Df Residuals: 2240688 BIC: 4.805e+07 \n", " \n", "\n", " Df Model: 2 \n", " \n", "\n", " Covariance Type: nonrobust \n", " \n", "
\n", "\n", "\n", " coef std err t P>|t| [0.025 0.975] \n", " \n", "\n", " AGE 310.9887 0.600 518.682 0.000 309.814 312.164 \n", " \n", "\n", " M 4710.0290 14.658 321.323 0.000 4681.299 4738.759 \n", " \n", "\n", " one 4927.2190 26.032 189.272 0.000 4876.196 4978.242 \n", " \n", "
\n", "\n", "\n", " Omnibus: 132298.944 Durbin-Watson: 0.183 \n", " \n", "\n", " Prob(Omnibus): 0.000 Jarque-Bera (JB): 158649.997 \n", " \n", "\n", " Skew: 0.614 Prob(JB): 0.00 \n", " \n", "\n", " Kurtosis: 3.440 Cond. No. 150. \n", " \n", "
Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."], "text/plain": ["\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: montant R-squared: 0.140\n", "Model: OLS Adj. R-squared: 0.140\n", "Method: Least Squares F-statistic: 1.817e+05\n", "Date: Sat, 12 Feb 2022 Prob (F-statistic): 0.00\n", "Time: 18:57:08 Log-Likelihood: -2.4023e+07\n", "No. Observations: 2240691 AIC: 4.805e+07\n", "Df Residuals: 2240688 BIC: 4.805e+07\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "AGE 310.9887 0.600 518.682 0.000 309.814 312.164\n", "M 4710.0290 14.658 321.323 0.000 4681.299 4738.759\n", "one 4927.2190 26.032 189.272 0.000 4876.196 4978.242\n", "==============================================================================\n", "Omnibus: 132298.944 Durbin-Watson: 0.183\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 158649.997\n", "Skew: 0.614 Prob(JB): 0.00\n", "Kurtosis: 3.440 Cond. No. 150.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\""]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["import statsmodels.api as sm\n", "nonull[\"one\"] = 1.0 # on ajoute la constante\n", "model = sm.OLS(nonull.montant, nonull [[\"AGE\",\"M\", \"one\"]])\n", "results = model.fit()\n", "print(\"coefficients\",results.params)\n", "results.summary()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On reproduit le m\u00eame dessin :"]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": ["import random\n", "val = nonull.copy()\n", "val[\"rnd\"] = val.apply(lambda r: random.randint(0,1000), axis=1)\n", "ech = val[val[\"rnd\"] == 1]"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["homme = ech [ ech.M == 1]\n", "femme = ech [ ech.M == 0]\n", "\n", "predh = results.predict(homme[[\"AGE\",\"M\",\"one\"]])\n", "predf = results.predict(femme[[\"AGE\",\"M\",\"one\"]])\n", "\n", "import matplotlib.pyplot as plt\n", "def graph(homme, femme, predh, predf):\n", " fig, ax = plt.subplots(1, 1, figsize=(16, 6))\n", " ax.plot(homme.AGE, homme.montant, \"r.\")\n", " ax.plot(femme.AGE + 0.2, femme.montant, \"b.\")\n", " ax.plot(homme.AGE, predh, \"ro-\", label=\"hommes\")\n", " ax.plot(femme.AGE, predf, \"bo-\", label=\"femmes\")\n", " ax.legend()\n", " ax.set_title(\"Montant moyen par \u00e2ge et genre\");\n", " return ax\n", "\n", "graph(homme, femme, predh, predf);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On ajoute l'intervalle de confiance sur un \u00e9chantillon :"]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"data": 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\n", " \n", " \n", " \n", " AGE \n", " M \n", " F \n", " montant \n", " one \n", " rnd \n", " pred \n", " up \n", " down \n", " \n", " \n", " \n", " \n", " 86 \n", " 34.0 \n", " 0 \n", " 1 \n", " 750.0 \n", " 1.0 \n", " 1 \n", " 10964.546920 \n", " 36990.964539 \n", " -5989.292820 \n", " \n", " \n", " 355 \n", " 48.0 \n", " 0 \n", " 1 \n", " 750.0 \n", " 1.0 \n", " 1 \n", " 10964.547492 \n", " 41344.807893 \n", " -1635.451707 \n", " \n", " \n", " 1046 \n", " 68.0 \n", " 0 \n", " 1 \n", " 750.0 \n", " 1.0 \n", " 1 \n", " 10964.559457 \n", " 47564.605962 \n", " 4584.299462 \n", " \n", " \n", " 2008 \n", " 21.0 \n", " 0 \n", " 1 \n", " 750.0 \n", " 1.0 \n", " 1 \n", " 10964.552144 \n", " 32948.121273 \n", " -10032.156559 \n", " \n", " \n", " 3761 \n", " 18.0 \n", " 1 \n", " 0 \n", " 750.0 \n", " 1.0 \n", " 1 \n", " 10964.553468 \n", " 36725.186695 \n", " -6255.096328 \n", " \n", " \n", "
\n", "
"], "text/plain": [" AGE M F montant one rnd pred up down\n", "86 34.0 0 1 750.0 1.0 1 10964.546920 36990.964539 -5989.292820\n", "355 48.0 0 1 750.0 1.0 1 10964.547492 41344.807893 -1635.451707\n", "1046 68.0 0 1 750.0 1.0 1 10964.559457 47564.605962 4584.299462\n", "2008 21.0 0 1 750.0 1.0 1 10964.552144 32948.121273 -10032.156559\n", "3761 18.0 1 0 750.0 1.0 1 10964.553468 36725.186695 -6255.096328"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "\n", "prstd, iv_l, iv_u = wls_prediction_std(results)\n", "\n", "val = nonull.copy()\n", "val[\"rnd\"] = val.apply(lambda r: random.randint(0, 1000), axis=1)\n", "val[\"pred\"] = prstd\n", "val[\"up\"] = iv_u\n", "val[\"down\"] = iv_l\n", "\n", "ech = val[val[\"rnd\"] == 1]\n", "ech.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Puis on l'ajoute au graphe pr\u00e9c\u00e9dent :"]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["homme = ech[ech.M == 1]\n", "femme = ech[ech.M == 0]\n", "\n", "predh = results.predict(homme[[\"AGE\",\"M\",\"one\"]])\n", "predf = results.predict(femme[[\"AGE\",\"M\",\"one\"]])\n", "\n", "ax = graph(homme, femme, predh, predf)\n", "ax.plot(homme.AGE, homme.up, 'r-')\n", "ax.plot(homme.AGE, homme.down, 'r-')\n", "ax.plot(femme.AGE, femme.up, 'b-')\n", "ax.plot(femme.AGE, femme.down, 'b-')\n", "ax.set_title(\"Montant moyen par \u00e2ge et genre avec \u00e9cart-type\");"]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": 24, "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": "3.9.5"}}, "nbformat": 4, "nbformat_minor": 2}