From 9fe561e5e3b0a6c734714269db0dd43ebef37b2c Mon Sep 17 00:00:00 2001 From: Philippe Roy Date: Tue, 20 Jun 2023 12:07:47 +0200 Subject: [PATCH] =?UTF-8?q?Fondamentaux=20:=20r=C3=A9gression=20logistique?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- fondamentaux/01-regression_lineaire.py | 2 +- fondamentaux/06-regression_logistique.py | 91 ++++++++++++++++++ fondamentaux/README.md | 5 + fondamentaux/img/06-regression_logistique.png | Bin 0 -> 400792 bytes 4 files changed, 97 insertions(+), 1 deletion(-) create mode 100644 fondamentaux/06-regression_logistique.py create mode 100644 fondamentaux/img/06-regression_logistique.png diff --git a/fondamentaux/01-regression_lineaire.py b/fondamentaux/01-regression_lineaire.py index d09ad01..14a5a4a 100644 --- a/fondamentaux/01-regression_lineaire.py +++ b/fondamentaux/01-regression_lineaire.py @@ -87,7 +87,7 @@ y_predict_skl=model_skl.predict(x1_new) # Prédiction # Résultats ############################################################################### -# Plot +# Plot des données donnees_ax.set_title("Données") donnees_ax.plot(x1_new, y_predict, 'r-', label="Prédictions") donnees_ax.plot(x1_new, y_predict_skl, 'y-', label="Prédictions - Scikit-Learn") diff --git a/fondamentaux/06-regression_logistique.py b/fondamentaux/06-regression_logistique.py new file mode 100644 index 0000000..9d04a62 --- /dev/null +++ b/fondamentaux/06-regression_logistique.py @@ -0,0 +1,91 @@ +import time +import numpy as np +import sklearn +import matplotlib.pyplot as plt + +############################################################################### +# 06-regression_logistique.py +# @title: Apprentissage par régression logistique +# @project: Mes scripts de ML +# @lang: fr +# @authors: Philippe Roy +# @copyright: Copyright (C) 2023 Philippe Roy +# @license: GNU GPL +############################################################################### + +### +# Commandes NumPy : +# - np.array : créer un tableau à partir d'une liste de listes +# - np.linspace : créer un tableau 1D de la valeur de début à la valeur de fin avec n valeurs +# - .reshape : reformater la tableau avec le nombre de lignes et le nombre de colonnes +### + +### +# Commandes Scikit-Learn : +# - sklearn.linear_model.LogisticRegression : créer un modèle de régression logistique (méthode des moindres carrés) +# - .fit : entrainement du modèle +# - .predict : prédiction du modèle +### + +############################################################################### +# Initialisation +############################################################################### + +# Init du temps +t_debut = time.time() + +# Init des plots +fig = plt.figure(figsize=(15, 5)) +fig.suptitle("Régression logistique") +donnees_ax = fig.add_subplot(111) # Observations : x1 et cibles : y +# donnees_ax = fig.add_subplot(141) # Observations : x1 et cibles : y +# model_ax = fig.add_subplot(142) # Modèle : theta0, theta1 +# couts_ax = fig.add_subplot(143) # Coûts : RMSE, MSE, ... +# app_ax = fig.add_subplot(144) # Taux d'apprentissage : eta + +############################################################################### +# Observations +############################################################################### + +# Observations d'apprentisage +iris = sklearn.datasets.load_iris() # Jeu de données Iris +x1 = iris['data'][:, 3].reshape(-1, 1) # Largeur de pétale, reshape : -1 -> nb de ligne automatique +y = (iris["target"] == 2).astype(np.int32) # Si Iris virginica -> 1 sinon -> 0 +donnees_ax.plot(x1[y==0], y[y==0], "bs", label="Observations - Iris non-virginica") +donnees_ax.plot(x1[y==1], y[y==1], "g^" , label="Observations - Iris virginica") + +# Nouvelles observations +x1_new=np.linspace(0, 3, 1000).reshape(-1, 1) # reshape : -1 -> nb de ligne automatique + +############################################################################### +# Phase d'apprentissage +############################################################################### + +model = sklearn.linear_model.LogisticRegression() # Modèle régression logistique +model.fit(x1, y) # Entrainement + +############################################################################### +# Phase d'inférence +############################################################################### + +y_predict=model.predict(x1_new) # Prédiction +y_proba = model.predict_proba(x1_new) # Probabilité +frontiere_decision = x1_new[y_proba[:, 1] >= 0.5][0][0] + +############################################################################### +# Résultats +############################################################################### + +# Plot des données +donnees_ax.set_title("Frontière de décision") +# donnees_ax.plot(x1_new, y_predict, 'r-', label="Prédictions") +donnees_ax.plot(x1_new, y_proba[:,1], 'b:', label="Probabilité - Scikit-Learn") +donnees_ax.plot(x1_new, y_predict, 'y-', label="Prédictions - Scikit-Learn") +donnees_ax.set_xlabel(r'$x_1$'+" - Largeur de pétale") +donnees_ax.set_ylabel(r'$y$'+" - Probabilité d'être Iris virginica") +donnees_ax.legend() +plt.show() + +# Performances +print ("Frontière de décision : "+str(round(frontiere_decision, 6))) +print ("Temps : "+str(time.time()-t_debut)) diff --git a/fondamentaux/README.md b/fondamentaux/README.md index 31366c7..7a335c7 100644 --- a/fondamentaux/README.md +++ b/fondamentaux/README.md @@ -20,5 +20,10 @@ ![capture d'écran](img/05-regression_polynomiale.png) +### Classificateur par régression logistique + +![capture d'écran](img/06-regression_logistique.png) + + diff --git a/fondamentaux/img/06-regression_logistique.png b/fondamentaux/img/06-regression_logistique.png new file mode 100644 index 0000000000000000000000000000000000000000..feb0bc8b9550028c252cab7a3a2c8481e20976cf GIT binary patch literal 400792 zcmb@uWmH^Evo^d58VC?91P_GZ8r%ax2X}`M0t9#0gaE-^X0YH48e9hmBoN%)-Q6Yd z?fbr;=Q-!R=g+s+`>r)>*wgIk-Q87nRn^ti6RM&lgN;dw2><}LoUEi80H6l|017|G zBe2DyLW>amgX}CJr-6ZiF}tk11OP7pIZ1I1kF>pccLRb=GT>;ll0dp04NoN)8L#du zL0x_6Y~!U->w5~N z`*Y^p(0sYze28!lrng^ssaIo#ib4J5sl>nER6I>JU4egp`n$bFksOv^EQt2s?s_V* z_(I{$%m3a|^pqbJ{XgA+U(CY%QD%hj((DTOS1+cW5#Z-UIDES!BwDl}!=xkl{-k#h z4-fD7_;?+E>iTpu8291+WS#jnw8Vgkg*}z;f!As6Hx@Blkw#Hb6zbpO%yMdj?*M;y zsX|m$Q9&Z+mlhHdQc)3Wz^1IIXxQMS`To6{W|!t0T72J2!`7=S54v#Qqa{BqEUXPy zI5#s3i_KsLVm&eC-iM8C;H7j#q4r>Uc41-R*4q#qgwJ)61E-MC?D1+>Q`6-7=`}TV zwEMx`OA!$YU?5Fkb@TG=;Q}IxN=$XT;-C3z&=9j{pa)Ep&ca}@*VdI)Re_)FkltJ# zh`3^|k`Mj8ln<`q&sbgD%C1+VPhZk32nq^bY>%dPttKB9{2YY$cNL_|(hBc%5#PiU*=akqcx?N0JiIE2$Na2U|^5npLHy?pB)h9xnQSBhYN{e|~X(F5-2j zqoAOWDTcPOnO-=l9G~lH@j&4O~h<>z3+*Hd1%?$*%=tNE*ISkHHw0;HtQ@$ zH=@Pd(gmEK0UQ0N){6@klV$S-%9)O?u23H9q$+2{{$NrqEiKcosN=Je!PR4F{T0yaF~l?g2WGr$$10bCzs8!8z)vqpu%L#@E@=5jeOhBPDgX*qTYw zUP6&OKsQrsH{Z!(fFR}YI%CLE`DkVajfp8V%r_Eo2esi6ywmd(4Ty`6H#9UP<+CHl z$EQi|eMv*JGhV#8wnj)mpuV^L_MeqJsj(Ul?&e=?aMrKtE18T4ooEYrO7oYk{3&20 zgoI_+8yrMYv?{0ZXL+8Q8?b2?^wn5TqT`U0ll!zzOhDH zS{nAq{a}uQ-{HH`1qqOrZvE(ujg7%D`g?}l#{_gzlFg_AX1z(bHy1-6GLe>c#sfYf zqlbjFS}ru}MpMos1$3>J8s0jm^4jv+OmX|(D0tsmi9IxTc6YAGI5fULOSvxr$@beb}9Uvy&%#J4h$6yDScV`2y%b)yZAGJ2qFx`m{nV_5u z`EQ^63o?fM4gCD>mpf#nBc9vY+Rm37FZ%tt$iiR51_!hqN$MI$o^h++y6`IGmDFv)4dg@UCvHUm@*%2Y@Ps~o}PkK2D&U- zgBc>g3ul3p-H=9}c=#u$&lG0e>wczb*E@$s=GTbr69 z1Nfm|;H>QI(E?U?-=h9TH>Rk8g8mV|D{da1^9wuI!v%_sygGO03Z0TVd~&47zwS9R zcE37`-5#|d7xSZGV!{MUN=kyCkdzp{U}ly-_2=T^;!RZ?JxgTPM*8NwsZ^?0gA$-$ z_ZbNA6#k|GJ2!a?&&Rj6CIcMY-7ha&w+{U)44Z0!clO4L>Myvs)a^a`IILo2a}gth zhap(R_xtlrmk944;l%8!aE)LIrbh(yT%O$FQb0gWO^w@>8k`VFL8$;ItzY@U@^HbI z&t~f5hY!_G>tR;odzgpr+aqQm*tgy{+AX(VDqul99Noc_P3dX7i9hOhHZ=>VmwL%3#%8WqTN%K>sP8OMkeA1L{FoCIHXk1@E-vT; zQ3GOPy42v+pznwz|5)F%vF4rDJxbA8_`qb9n?>dmCd%iQy%-fER zOZh0EpunQy)p;-qFg>jcytB5}x4{O#15r?Lax0szcQn=4p9Nv2aC`^U0HP-9j$gk# zZ_X@naBzev%YD76K+V)(GH%5|59m+jGi>pJE2i?!tOrRfyDy$QySumg3NZ)^lLIJ^ z#6d*_k@WcSm%k-1+qK@C{N#^zckD-t%wO*vL09QIVcmiEFV|@a$y3WyQ-Z$MrHOrt z!VFqN+owR#dko(vOu^N=l+p!zKD-8{e|t$a)a<$)=U-*s54_+wdRC>zmdvhK?Jct}GcW;VQR8$ni zW0qQn9-bYKzAjZVU#bMgF*v?Mq)yy%3K@=55oYuBL z;7d7QSYY)KUdWTbCzK*`ocJ37o{nf0k=rM;&G(oU7#S_}H4lQ22ws5RXq4>naJA*A z(=^rJA=k~t)vfV|3Z0H%Y>SPFp&#o`gXuyY?<;~q&q-oufCt7^ervz+jCQHNH6{}ysUD~Ds#nY8#h^5S*ZdqTna&- zt)XRhrd-U|zBds?I7t7X`^lHb|JbR~*kCwtSj(rob?;er<{g8jXsRXs5aQ`#RzZC}zF+ct5;s5>) z(Px58c;U|Te?8@&+k6B0fu-T0;-Zc>|1|v5-U0Lv<;6>g(*gCf4!PLRr#y7Df@zfa;Gc0^Tb}kXd`E zu0G6Om#eiX=MAuU(=gP+)XI?P6VBjXOg!M?R%9fe=wA_eS~K_VZ`}B+|Fd5!3C&w0 zRCJo<=Ea`ZryH#+1nN=g$LyCWXqWpF@Y3XsJUvAvXkCxW@>czoPs1aCrSDhq#vnuX z2RsGEh~TsHQuSiG%wEObn1#m6t(kpvv%-RGUm{1|5@U;*lpnyGxDOD$&gSmgU@%b+&*d|V5JgpAL3+(<(>Vy;R;O(4c(7= z`eiDezvwB1R;g}9D`?5kLWBpCd*y4&2OA1`rfen#&cm_F7rpoT=FyeK3N}Vy0wz

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