sklearn上机笔记3:朴素贝叶斯算法实践

import pandas as pd
import numpy as np

#导入数据集生成工具
from sklearn.datasets import make_blobs

#随机生成500个类别数是5的分类数据
X,y = make_blobs(n_samples = 500,centers = 5,random_state = 5)

#数据可视化
import matplotlib.pyplot as plt
%matplotlib inline
plt.scatter(X[:,0],X[:,1],c = y,cmap = plt.cm.spring,edgecolors = 'k')

#拆分训练集与测试集
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 8)

#导入朴素贝叶斯分类器
from sklearn.naive_bayes import BernoulliNB,MultinomialNB,GaussianNB

#构建伯努利分布（二项分布）朴素贝叶斯分类器
nb = BernoulliNB()
nb.fit(X_train,y_train)
print('模型得分：{:.3f}'.format(nb.score(X_test,y_test)))

#拟合结果可视化
plt.figure(dpi = 300)
x_min,x_max = X[:,0].min()-0.5,X[:,0].max()+0.5
y_min,y_max = X[:,1].min()-0.5,X[:,1].max()+0.5
xx,yy = np.meshgrid(np.arange(x_min,x_max,.02),np.arange(y_min,y_max,.02))
z = nb.predict(np.c_[(xx.ravel(),yy.ravel())]).reshape(xx.shape)
plt.pcolormesh(xx,yy,z,cmap = plt.cm.Pastel1)
plt.scatter(X_train[:,0],X_train[:,1],c = y_train,cmap = plt.cm.cool,edgecolor = 'k')
plt.scatter(X_test[:,0],X_test[:,1],c = y_test,cmap = plt.cm.cool,marker = '*',edgecolor = 'k')
plt.xlim(xx.min(),xx.max())
plt.ylim(yy.min(),yy.max())
plt.title('Classifier:BernoulliNB')
plt.show

#构建多项分布朴素贝叶斯分类器
mnb = MultinomialNB()
mnb.fit(X_train,y_train)
print('模型得分：{:.3f}'.format(mnb.score(X_test,y_test)))

array([[-1.03627407e+00,  2.08982548e+00],[-2.93381863e+00, -5.99131201e+00],[ 1.53703669e-01,  1.80568727e+00],[-5.91896553e+00,  8.01811773e+00],[-5.98814699e+00,  7.56447506e+00],[-5.43845287e+00,  1.09607877e+01],[-6.07039263e+00,  7.93190548e+00],[-6.72483849e+00,  6.01757210e+00],[ 5.67661970e+00,  1.68576076e+00],[-5.45939839e+00,  7.77008460e+00],[-1.09394449e-01,  3.92488330e+00],[-4.22640456e+00,  7.13207352e+00],[-9.14953234e+00,  7.59352491e+00],[-4.71382623e-01,  2.96332032e+00],[ 3.48826186e-01,  3.27154564e+00],[-4.11625547e+00,  7.85760238e+00],[-7.71636615e-01,  1.31043975e+00],[-5.36247649e+00,  8.74949470e+00],[ 5.12176441e-02,  2.08644338e+00],[-9.31289497e-01,  3.17416541e+00],[-8.64020025e+00,  9.45084629e+00],[-7.37110801e-02,  1.03163659e+00],[-3.42815006e+00, -6.08016684e+00],[-3.29107834e+00, -7.02117162e+00],[ 2.27469586e-01,  2.26757693e+00],[-6.09377785e-01,  6.77576187e-01],[-5.92854611e+00,  5.16552800e+00],[ 1.65239303e+00,  4.92244975e-01],[ 3.86093790e+00, -6.13310338e-02],[ 5.49555049e+00, -4.65162941e-01],[-4.42079360e+00,  7.90908652e+00],[-4.11128057e+00,  8.58678214e+00],[-4.14882577e+00, -7.23254810e+00],[ 4.53784583e+00,  4.03497041e-01],[-3.52122094e+00, -5.84466313e+00],[-6.54627096e+00,  7.74771477e+00],[ 6.15562083e+00,  7.43865304e-01],[-4.14673856e+00,  7.63590025e+00],[-7.15983508e+00,  8.29179894e+00],[ 4.78292494e+00,  1.62539127e+00],[-3.85750611e+00, -6.24217718e+00],[-6.58069814e+00,  8.32097963e+00],[ 4.98183594e-01,  1.42099539e+00],[ 6.67958015e+00,  3.64092415e-01],[-6.01081708e+00,  9.33601946e+00],[ 1.13226528e+00,  1.15224609e+00],[ 4.42094483e+00,  8.45447097e-02],[-4.78353050e+00, -6.57019545e+00],[-5.34959412e+00,  7.76114787e+00],[ 5.93088467e+00,  2.60033155e+00],[ 4.72301753e+00, -2.03095558e+00],[-3.47085190e+00, -6.74846176e+00],[-2.90477499e+00, -6.07471689e+00],[-2.74432946e+00, -5.90493130e+00],[ 3.53344174e+00,  1.73776474e+00],[ 6.13909995e+00,  1.57580589e-01],[-6.54074446e+00,  6.55779297e+00],[-7.34128788e+00,  6.66674034e+00],[-5.10247851e+00,  9.65261174e+00],[-4.33239321e+00, -5.77802757e+00],[-3.01907353e-01,  7.29354105e-01],[-3.67716795e+00, -4.66248249e+00],[-6.58610935e+00, -5.37570900e+00],[-3.67278957e+00, -5.86688286e+00],[ 6.74418062e+00,  1.38146677e+00],[-6.03143291e+00,  1.10175716e+01],[-1.16476066e+00,  1.89415337e+00],[ 4.82647422e+00,  1.37239594e+00],[ 1.11073566e+00,  1.23554652e+00],[ 5.50675281e+00,  1.34128510e+00],[ 6.50478954e+00,  6.75023523e-01],[-5.70326300e+00,  7.19045629e+00],[-5.15372763e+00,  9.56937265e+00],[-4.90872904e+00, -6.06196714e+00],[-6.87090971e+00,  6.72508089e+00],[-6.43201576e+00,  6.99213819e+00],[-5.55684774e+00,  7.30871568e+00],[-3.68125834e+00, -6.33217690e+00],[-4.67507467e+00, -7.30537131e+00],[-2.78926635e+00, -5.29373748e+00],[-4.42266688e+00,  7.12822302e+00],[-7.13446720e-01,  1.54646112e+00],[ 3.67940653e+00, -1.52275822e-01],[-6.55573288e+00,  8.32153559e+00],[-5.99791884e+00,  7.99299317e+00],[ 5.68933630e+00, -7.12355443e-02],[-4.51671456e-01, -5.95011337e+00],[-3.65131604e+00, -5.76560647e+00],[-3.03652578e+00, -5.02856458e+00],[-4.81298399e+00,  8.87774160e+00],[ 5.53632702e+00, -3.16435185e-01],[ 6.40980851e+00,  1.88022974e+00],[-3.36856057e-01,  3.53169809e+00],[ 5.85349687e+00,  1.32617842e+00],[ 5.65560012e+00,  5.08112464e-01],[-4.54171545e+00,  8.47599756e+00],[-6.10605373e+00,  8.33500989e+00],[ 5.02446463e-01,  3.17858691e+00],[-4.60387472e+00,  6.77613557e+00],[-6.79424341e+00,  8.33674567e+00],[ 4.90290504e+00,  1.15953036e-01],[-3.46513055e+00, -3.79191728e+00],[-6.06907050e+00,  7.36467063e+00],[-5.98612354e+00,  7.79339238e+00],[-1.13280383e+00,  3.76709024e+00],[-6.40660520e+00,  9.93545376e+00],[-4.65002601e+00,  8.03249087e+00],[-5.06479804e+00,  8.14913489e+00],[-4.34785317e+00,  6.77999362e+00],[-5.21272388e+00,  8.87805795e+00],[ 6.08166339e+00,  4.58563299e-01],[ 4.13092156e+00, -3.34519101e-01],[-3.29566791e+00, -5.44618653e+00],[-4.15796996e+00,  7.87974712e+00],[-5.43971773e+00,  8.50141839e+00],[ 5.53744937e+00,  2.55511792e+00],[-7.07131614e+00,  8.05949363e+00],[-4.68532279e+00, -5.59952347e+00],[-6.12718775e+00,  7.82832493e+00],[ 4.83162025e+00,  1.53661146e+00],[-6.89519039e+00,  7.03593785e+00],[-5.88297016e+00,  8.24455994e+00],[-3.69908143e+00, -5.49610781e+00],[-4.89126094e+00, -6.28401113e+00],[-2.86938995e+00, -6.05384585e+00],[-6.03197750e+00,  9.72533127e+00],[ 5.55304164e+00,  2.88076235e-02],[-4.10486015e+00, -4.54412492e+00],[ 1.20093592e+00,  1.59325363e+00],[-4.20031328e+00, -6.84310921e+00],[-6.55941624e+00,  8.79141592e+00],[-7.04139578e+00,  7.39770081e+00],[ 1.09135945e-01, -4.07771243e-02],[-5.15066695e+00,  7.56876556e+00],[-2.93880221e+00, -6.42300135e+00],[-7.31387744e+00,  8.04494260e+00],[-2.75242269e+00, -4.67616705e+00],[-6.72248504e+00,  9.18753205e+00],[-4.41862884e+00,  6.91289058e+00],[ 9.61072369e-01,  2.48098519e+00],[ 4.99336838e+00,  1.07471525e-01],[ 6.70703299e-01,  1.06837795e+00],[-2.70054287e+00, -8.25168407e+00],[-4.74400422e+00,  7.13321600e+00],[-5.06161295e+00,  6.70855225e+00],[-7.32031076e+00,  8.22768350e+00],[-5.10654574e+00, -4.90564007e+00],[-9.86237622e-01,  4.40463885e+00],[-6.11508677e+00,  7.65488026e+00],[-3.88127035e+00, -6.85438172e+00],[-1.10419043e-01,  1.82344340e+00],[-3.16760093e+00, -4.89767958e+00],[ 5.65993961e+00, -3.22636373e-01],[-2.91801800e+00, -6.94670006e+00],[-1.26574219e+00,  2.76041480e+00],[-1.86793035e-01,  1.43670091e+00],[-3.34428450e+00, -6.26718033e+00],[ 1.31420929e-01,  2.24772601e+00],[-4.09840466e+00, -6.03209341e+00],[ 7.62501126e+00, -1.39886914e-01],[ 1.01276456e+00,  4.12128064e-01],[ 3.76673626e+00,  5.29131833e-01],[-6.51268537e+00,  8.85092421e+00],[-4.90351711e+00,  7.53945295e+00],[-5.53592583e+00,  7.17466439e+00],[-6.74130281e+00,  7.83410884e+00],[-6.30710126e-02,  3.50215408e+00],[-6.81284245e+00,  7.54523074e+00],[ 4.96234066e+00,  3.68515201e-03],[-6.13764981e+00,  8.56685089e+00],[-4.83413190e-01,  1.02643040e+00],[-4.11016643e+00, -7.24415773e+00],[-5.38667971e+00, -5.74419446e+00],[-5.44174930e+00,  7.98488680e+00],[-3.53219906e+00, -4.15010942e+00],[ 6.66121772e+00,  7.72278557e-01],[ 5.84981638e+00,  5.01954981e-01],[ 7.03931680e-01,  1.05497409e+00],[-7.24594912e+00,  1.04739948e+01],[-7.64328346e-01,  2.84723332e+00],[-6.17065298e+00,  5.71076072e+00],[-6.59863182e+00,  7.24293708e+00],[-4.19954247e+00, -5.50080597e+00],[ 5.88979728e+00,  2.73814548e-01],[-1.42740056e+00,  2.06290305e+00],[-6.31207362e+00,  8.32242947e+00],[ 6.58105944e-01,  2.04889479e+00],[ 4.55636085e+00,  3.86132419e-01],[-5.50099233e+00,  7.05133525e+00],[-6.04941632e+00,  9.58695589e+00],[ 4.85416274e-01,  2.93264837e+00],[-4.37107585e+00,  7.34105280e+00],[-5.42657952e+00, -5.45794209e+00],[-4.35197113e+00,  8.51327473e+00],[-4.62757607e+00,  7.62668944e+00],[ 3.88125726e+00,  1.06641732e+00],[-5.08731948e+00, -5.02290814e+00],[ 5.47436675e+00,  1.43169082e+00],[-5.42580589e+00, -6.07904815e+00],[-4.33557404e+00, -6.53570948e+00],[-1.04699636e+00,  4.56008765e+00],[ 8.30577605e-01,  2.70838228e+00],[-4.91527219e+00,  8.93521946e+00],[-1.40541758e-01,  3.36929625e+00],[-5.00597094e+00,  8.08472155e+00],[-4.08929476e+00,  7.79908497e+00],[-5.33685899e+00, -8.46747317e+00],[ 6.48948865e+00, -2.05466917e-01],[ 4.84737162e+00,  1.03073578e-01],[-8.22193540e-01,  2.20352627e+00],[-4.81440963e+00,  8.19155371e+00],[-5.80084772e+00,  6.59052267e+00],[ 5.50000930e+00,  3.34976443e+00],[-6.41511423e+00,  8.74330511e+00],[-3.39319656e+00, -5.94956317e+00],[ 6.38254413e+00,  8.64979337e-02],[ 6.02821648e-01,  2.18073598e+00],[ 4.67269922e+00,  8.28652638e-01],[-5.44034860e+00, -6.40299619e+00],[-7.90702495e-01,  1.08587728e+00],[ 5.76299098e+00, -4.64326863e-01],[-6.21492502e+00,  7.93147970e+00],[-4.40874557e+00,  9.27197713e+00],[-5.61244473e+00,  7.66386378e+00],[ 3.72731521e+00,  5.78302532e-01],[-6.15353935e+00,  8.20288407e+00],[-5.13247641e+00, -6.54865461e+00],[-5.09243136e+00,  7.51599091e+00],[ 4.56947448e-02, -2.28256005e-02],[-2.25553937e-01,  1.84875253e+00],[ 6.26142652e+00,  1.43777636e+00],[-2.94533245e-02,  2.91357681e+00],[-7.49529529e+00,  9.50708153e+00],[-6.23548902e+00,  9.05646497e+00],[-6.44955731e+00,  6.11369467e+00],[-2.74101107e+00,  1.21385490e+00],[-4.60754329e+00,  7.23332790e+00],[-6.00556657e+00,  6.93252594e+00],[-3.20422024e+00, -7.02632763e+00],[-4.36848581e+00, -4.41440570e+00],[ 1.72181246e+00,  7.94243760e-01],[-3.32686576e+00,  7.37994402e+00],[-7.16823832e+00,  7.80212851e+00],[-3.82168776e+00,  8.87985101e+00],[-2.47269367e+00, -6.97595703e+00],[-6.62516920e+00,  7.80506673e+00],[-3.74708346e+00, -6.13391785e+00],[ 6.90011486e-01,  1.83928564e+00],[-5.51953342e+00,  7.86590803e+00],[-5.82053874e+00,  7.60847127e+00],[-5.86114171e+00,  6.29623969e+00],[ 5.27505208e+00,  2.79062096e-01],[ 6.19549973e+00,  4.36860941e-01],[-4.33796602e+00,  6.60073412e+00],[ 6.52188839e+00, -5.10031634e-01],[-8.88108604e-01,  1.83052756e+00],[-5.29052417e+00,  8.70660951e+00],[-5.80020733e+00,  9.65866390e+00],[-4.68748196e+00,  7.21252795e+00],[-6.74039069e+00,  7.81513878e+00],[-9.27387170e-01,  6.11716671e-01],[ 1.41625780e+00,  3.62648489e+00],[ 3.77108045e-01,  3.00806197e+00],[-7.01584853e-01,  2.22711880e+00],[-4.52167076e+00,  8.63375773e+00],[-5.23673656e+00,  7.94662603e+00],[-7.67353352e+00,  9.36618714e+00],[-2.89423843e+00, -5.68609523e+00],[ 5.41635125e+00,  1.42148565e+00],[-4.06490707e+00, -5.22313870e+00],[-5.58825678e+00,  7.17777219e+00],[-5.08379040e+00,  7.00046786e+00],[-8.92499849e-01, -4.95602620e+00],[-4.63262037e+00,  9.05460019e+00],[-3.12716612e-01,  1.46164917e+00],[ 5.40345503e+00,  6.35656711e-01],[ 5.85621957e+00,  6.10747738e-01],[-5.37253335e+00,  7.08477617e+00],[-4.03871880e+00, -6.49219654e+00],[ 4.66831765e+00, -4.98377899e-01],[-3.80192666e+00, -6.38328493e+00],[-7.89575840e+00,  6.24736767e+00],[-2.83681714e-02,  9.58712097e-01],[-3.05156923e+00, -6.90616002e+00],[-2.47091686e-02,  2.75666100e+00],[-5.51418135e+00,  6.92751348e+00],[ 1.04521555e-01,  1.47325529e+00],[-3.46379451e+00, -7.20634207e+00],[-5.56633149e+00,  7.31357851e+00],[ 3.98290577e-01,  1.96107554e+00],[ 5.39230051e+00,  1.41787406e+00],[-3.10684994e+00, -3.92617296e+00],[-6.82421992e+00,  6.84143057e+00],[-4.95055958e+00, -7.35692418e+00],[-4.72419262e+00,  7.70204977e+00],[-4.97061595e+00,  8.19601479e+00],[-5.06180736e+00,  8.43278373e+00],[-5.93568392e+00,  8.67044528e+00],[-4.46964234e+00, -6.83163994e+00],[-5.99983389e+00,  9.20481177e+00],[-5.48980211e+00,  8.14623399e+00],[ 4.72517215e+00,  2.86244207e-01],[ 5.43443681e+00, -8.67473006e-01],[ 5.64461341e+00, -1.24661413e+00],[-4.72079556e-01,  4.47583083e+00],[-5.61948856e+00,  5.02794838e+00],[-6.75290119e+00,  7.20976961e+00],[-4.56369675e+00,  8.12706739e+00],[-5.14082991e+00, -5.13401909e+00],[-7.35674575e+00,  7.09011068e+00],[ 5.27592960e+00,  3.32878392e-01],[ 6.96639806e+00,  8.81773249e-01],[ 4.24594204e+00, -4.95218409e-02],[-6.44304683e+00,  7.13748866e+00],[-6.98690449e+00,  9.84506289e+00],[-6.79989549e+00,  9.28408057e+00],[-6.48898913e+00,  7.29151621e+00],[ 1.40493555e-01,  3.89692122e-01],[-5.99584050e+00,  8.38682543e+00],[-3.61528434e+00, -4.44083421e+00],[-6.79252269e+00,  8.14159938e+00],[ 7.69113617e-01,  9.20637744e-01],[-6.56296270e+00,  7.10891700e+00],[-1.30119185e+00,  1.56068746e+00],[-3.09509035e+00, -5.92389407e+00],[-5.23942429e+00,  8.83965022e+00],[ 5.52615515e+00,  1.13089484e+00],[-5.89647284e+00,  7.31403178e+00],[-3.48898653e+00, -6.00211592e+00],[-5.38693313e-03,  3.03400753e+00],[ 4.45277464e+00, -2.31009606e+00],[ 9.68384016e-01,  3.89162116e+00],[-4.59049797e+00, -7.14133954e+00],[ 4.39123059e+00,  7.97843992e-01],[ 5.94034133e+00,  3.41450215e-01],[ 4.70864686e+00,  1.82334522e+00],[-7.36647629e+00,  9.55796259e+00],[ 1.46777187e+00,  1.94866102e+00],[-8.41982454e+00,  8.20401253e+00],[-3.67896892e+00, -5.09557430e+00],[ 6.24351680e+00, -2.02551952e+00],[ 7.56596796e+00, -2.69342836e+00],[-6.20723193e+00,  7.44131768e+00],[ 5.86526091e+00,  1.47354865e+00],[-6.34156772e+00,  9.38312690e+00],[ 4.77711119e+00, -7.09985974e-01],[ 5.54915451e+00,  3.98031820e-01],[-2.82350256e+00, -5.94702454e+00],[-6.83975976e+00,  7.45118877e+00],[-5.18063098e+00,  8.01102397e+00],[-6.39582217e+00,  1.02279326e+01],[ 6.19870723e+00,  1.71574609e+00],[-3.47162189e+00,  7.76156545e+00],[-2.50774347e-01,  3.15053985e+00],[ 1.08491796e+00,  2.81307289e+00],[-3.80194623e+00,  8.38484166e+00],[ 5.18420128e+00, -7.97829107e-02],[-6.05341060e+00,  8.68768431e+00],[-1.34030228e+00,  3.40173995e-01],[-5.77126394e+00, -6.06565256e+00],[-5.04332861e+00, -7.25618362e+00],[-4.76771396e+00,  7.58541058e+00],[-4.24863809e+00,  6.64116453e+00],[ 5.51455599e+00, -2.07975085e-01],[-4.72218901e+00,  7.86314556e+00],[-5.64701218e+00,  8.97617842e+00],[-5.81158063e+00, -7.41643652e+00],[-1.96969731e+00,  1.76839605e+00],[-6.76271089e+00,  9.00493002e+00],[ 8.06399394e-01,  2.58092912e+00],[-4.50641592e+00,  6.67012819e+00],[ 5.34139759e+00,  7.97276442e-01],[-5.66730056e+00,  9.67475290e+00],[-2.93929297e+00, -7.17183989e+00],[-5.36569920e+00,  7.44288737e+00],[-4.30261045e-01,  2.80315031e+00],[-5.63928794e+00,  6.72181978e+00],[ 1.21308327e+00,  2.64313672e+00],[-5.90755324e+00,  9.44676094e+00],[-4.09352902e+00, -8.66450257e+00],[-5.36509965e+00, -6.77275387e+00],[ 4.27416888e-01,  2.15127564e+00],[-5.37892023e+00, -5.62060990e+00],[ 5.45813713e+00,  1.12535738e+00],[-3.53137680e+00,  8.58875011e+00],[-4.12925797e+00, -5.76188187e+00],[-4.65723882e+00,  9.06547186e+00],[-4.78167397e+00, -5.80682281e+00],[-4.23565906e+00, -7.17136669e+00],[ 4.20990848e+00,  5.61508007e-01],[-6.20649316e+00,  7.96321396e+00],[-5.16939490e+00,  9.27162362e+00],[-5.93575742e+00,  5.99272157e+00],[-5.33046174e+00,  7.89806575e+00],[-2.67665504e-02,  2.05522623e+00],[-7.22492511e+00,  6.71446709e+00],[-5.31031926e+00,  8.56184821e+00],[-3.73438476e+00, -5.93615315e+00],[-6.92771485e+00,  1.00385986e+01],[-6.27502057e-01,  1.08738281e+00],[-5.06051853e+00,  6.26310188e+00],[ 2.73828811e+00,  2.12478020e-01],[ 4.34987667e+00,  1.07073507e+00],[-2.59539311e+00, -6.15327641e+00],[-5.71000144e+00, -8.03923504e+00],[-6.09817312e+00,  9.70736850e+00],[-2.30067104e+00,  1.21267450e+00],[-7.43787746e+00,  8.95340227e+00],[-4.92598108e+00, -4.09855477e+00],[-6.17875540e+00,  7.76522862e+00],[-5.96765574e+00,  6.60202136e+00],[-3.76998995e+00, -6.63600313e+00],[-9.08752627e-01, -5.45455348e+00],[-6.27060303e+00,  7.19945832e+00],[ 3.08869568e+00,  4.09883185e-01],[ 4.57089520e+00, -1.09222773e+00],[ 5.53255687e+00,  3.71147228e-01],[-5.65396903e+00,  7.35897509e+00],[-5.91128719e+00,  8.42300481e+00],[-4.27907662e+00, -8.08433387e+00],[ 5.68789395e+00, -8.00277582e-01],[ 5.48250588e+00,  7.23068251e-01],[-5.81667226e+00,  6.34581627e+00],[-6.23356146e+00,  1.04837066e+01],[-4.86240733e+00,  7.59404055e+00],[-2.31358280e+00, -6.31775222e+00],[-1.59693200e+00,  1.44563824e+00],[ 3.87285501e+00,  9.43755465e-02],[ 9.74798112e-01,  7.34412016e-01],[ 3.18607151e+00, -7.03716662e-01],[-6.77613625e+00,  8.43768765e+00],[-6.24132430e+00,  8.40686203e+00],[-4.03995007e+00, -4.77005570e+00],[-4.99200386e+00,  7.42740444e+00],[-5.18187358e+00,  9.19846295e+00],[-1.78978255e+00,  3.46325730e+00],[ 5.47484128e-01,  1.21019267e+00],[ 1.12693101e+00,  2.83931494e+00],[-7.02423739e-01,  2.13697678e+00],[-6.47618425e+00,  8.89691242e+00],[-5.30265438e+00,  9.46898288e+00],[-4.10539650e+00, -7.06526384e+00],[-4.76708326e+00,  6.78307449e+00],[-6.32089689e+00,  6.70348290e+00],[-7.25381162e+00,  7.71829459e+00],[-7.14383259e+00,  7.37944871e+00],[ 5.75283019e+00, -3.05149778e-01],[-4.30723448e-01,  1.55135158e+00],[-5.54236346e+00, -6.42972475e+00],[-4.72512096e+00,  8.12448922e+00],[-5.31137507e-01,  2.93608217e+00],[-1.16133915e+00,  1.88984025e+00],[-5.86598960e+00,  6.93691471e+00],[ 5.53765293e-02,  3.07443905e+00],[-6.40609885e+00,  8.91839559e+00],[-4.67172224e+00, -6.36792995e+00],[-6.23416167e+00,  8.18047859e+00],[-4.10940303e+00,  8.02151644e+00],[ 5.96466344e+00,  1.16915888e+00],[ 5.64390046e+00, -1.34823997e+00],[ 4.32832343e+00,  9.02263933e-01],[-5.96912892e+00,  6.65981553e+00],[ 5.90751598e+00,  1.34354358e-02],[-3.97011310e+00, -5.51159001e+00],[-3.48652105e+00,  8.12444398e+00],[-1.28923814e+00,  3.39543627e+00],[ 6.76198273e+00,  7.23065358e-01],[ 1.08248307e+00,  2.62603961e+00],[-2.23956030e+00,  2.52030194e+00],[-3.73877184e+00,  6.98761474e+00],[ 4.06168988e+00,  2.19152321e+00],[-3.31682478e+00, -6.66363035e+00],[-2.99571309e-01,  2.00669127e+00],[-5.04593697e+00,  8.90385188e+00],[ 4.71482614e+00,  4.11411318e-01],[ 5.80249718e+00,  5.45054567e-01],[ 3.02298885e-01,  3.92920558e+00],[-7.67516812e+00,  8.11670042e+00],[-4.28635699e+00, -5.47494960e+00],[-9.79724724e-01,  1.06513011e+00],[-1.02630353e-01,  2.17297703e+00],[ 4.97157416e+00, -1.74026664e-01],[-1.46628056e+00,  1.94349352e+00],[-5.06657892e+00,  7.91513345e+00],[-8.89524528e-01,  2.30572948e+00],[-5.72478974e+00,  8.16221956e+00],[ 5.48448710e+00,  4.33251249e-01],[-5.10796309e+00, -6.23069986e+00],[-1.20784412e-01,  1.86480679e-01],[-5.07502328e+00,  9.50373213e+00],[-3.07274367e+00, -5.84723187e+00],[ 6.31921806e+00,  1.02375607e+00],[-3.79155412e+00, -7.11169782e+00],[ 6.18831365e+00,  6.05592618e-01],[ 2.18244472e-01,  1.57862792e+00],[-5.25453641e+00,  7.93649561e+00],[-3.29096914e+00, -6.30043116e+00],[ 4.39562854e+00,  4.63808680e-01],[-2.57415912e-01,  4.74476614e-01],[-6.15849651e+00,  7.17122642e+00]])

#导入数据预处理工具
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()

scaler.fit(X_train)

X_train_scaled = scaler.transform(X_train)
X_test_scaled  = scaler.transform(X_test)

#构建多项分布朴素贝叶斯分类器
mnb = MultinomialNB()
mnb.fit(X_train_scaled,y_train)
print('模型得分：{:.3f}'.format(mnb.score(X_test_scaled,y_test)))

#拟合结果可视化
plt.figure(dpi = 300)
z = mnb.predict(scaler.transform(np.c_[(xx.ravel(),yy.ravel())])).reshape(xx.shape)
plt.pcolormesh(xx,yy,z,cmap = plt.cm.Pastel1)
plt.scatter(X_train[:,0],X_train[:,1],c = y_train,cmap = plt.cm.cool,edgecolor = 'k')
plt.scatter(X_test[:,0],X_test[:,1],c = y_test,cmap = plt.cm.cool,marker = '*',edgecolor = 'k')
plt.xlim(xx.min(),xx.max())
plt.ylim(yy.min(),yy.max())
plt.title('Classifier:MultinomialNB')
plt.show()

#将X，y赋值为np数组 s=0; m=1 ; l=2; yes=1; no=0
X = np.array([[0,0,0],[0,2,1],[2,1,1],[1,1,1],[2,1,1],[1,2,0],[1,0,0],[2,1,0],[1,0,1],[0,0,1]])
y = np.array([0,1,1,1,1,1,0,1,1,0])

#构建多项式朴素贝叶斯分类模型
clf = MultinomialNB()
clf.fit(X,y)

#预测新账号类别
new_account = [[1,0,0]]
pre = clf.predict(new_account)
if pre == [1]:print("该账号可能是虚假账号！")
else:print("该账号为真实账号")

威斯康星乳腺肿瘤分类

#导入数据集
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()

cancer.keys

print('肿瘤的分类：',cancer['target_names'])
print('\n肿瘤的特征：\n',cancer['feature_names'])

print(len(cancer['feature_names']))

X,y = cancer.data,cancer.target
print(X.shape,y.shape)

#拆分训练集与测试集
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.25,random_state = 8)

print(cancer)

#构建高斯朴素贝叶斯模型
gnb = GaussianNB()

gnb.fit(X_train, y_train)

print('训练集得分：{:.3f}'.format(gnb.score(X_train, y_train)))
print('测试集得分：{:.3f}'.format(gnb.score(X_test, y_test)))

#随机选取一个样本进行测试
print('模型预测的分类是：{}'.format(gnb.predict([X[312]])))
print('样本的正确分类是：{}',y[312])

#输出预测的概率值
gnb.predict_proba([X[312]])

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sklearn上机笔记3:朴素贝叶斯算法实践

威斯康星乳腺肿瘤分类

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