2016-02-23 31 views
18

Metin belgelerinin (22000) 100 sınıftan sınıflandırılması için scikit-learn kullanıyorum. Karmaşıklık matrisini hesaplamak için scikit-learn'in karışıklık matris yöntemini kullanıyorum.Bir karışıklık matrisini nasıl çizebilirim?

model1 = LogisticRegression() 
model1 = model1.fit(matrix, labels) 
pred = model1.predict(test_matrix) 
cm=metrics.confusion_matrix(test_labels,pred) 
print(cm) 
plt.imshow(cm, cmap='binary') 

Bu benim karışıklık matris gibi görünüyor:

[[3962 325 0 ..., 0 0 0] 
[ 250 2765 0 ..., 0 0 0] 
[ 2 8 17 ..., 0 0 0] 
..., 
[ 1 6 0 ..., 5 0 0] 
[ 1 1 0 ..., 0 0 0] 
[ 9 0 0 ..., 0 0 9]] 

Ancak, berrak veya okunaklı arsa almazlar. Bunu yapmanın daha iyi bir yolu var mı?

cevap

13

How to plot confusion matrix with string axis rather than integer in python mükemmel cevap verir hata matrisi çizmek için Seaborn modül en heatmap kullanabilirsiniz. İşte

confusion matrix example

import numpy as np 
import matplotlib.pyplot as plt 

conf_arr = [[33,2,0,0,0,0,0,0,0,1,3], 
      [3,31,0,0,0,0,0,0,0,0,0], 
      [0,4,41,0,0,0,0,0,0,0,1], 
      [0,1,0,30,0,6,0,0,0,0,1], 
      [0,0,0,0,38,10,0,0,0,0,0], 
      [0,0,0,3,1,39,0,0,0,0,4], 
      [0,2,2,0,4,1,31,0,0,0,2], 
      [0,1,0,0,0,0,0,36,0,2,0], 
      [0,0,0,0,0,0,1,5,37,5,1], 
      [3,0,0,0,0,0,0,0,0,39,0], 
      [0,0,0,0,0,0,0,0,0,0,38]] 

norm_conf = [] 
for i in conf_arr: 
    a = 0 
    tmp_arr = [] 
    a = sum(i, 0) 
    for j in i: 
     tmp_arr.append(float(j)/float(a)) 
    norm_conf.append(tmp_arr) 

fig = plt.figure() 
plt.clf() 
ax = fig.add_subplot(111) 
ax.set_aspect(1) 
res = ax.imshow(np.array(norm_conf), cmap=plt.cm.jet, 
       interpolation='nearest') 

width, height = conf_arr.shape 

for x in xrange(width): 
    for y in xrange(height): 
     ax.annotate(str(conf_arr[x][y]), xy=(y, x), 
        horizontalalignment='center', 
        verticalalignment='center') 

cb = fig.colorbar(res) 
alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 
plt.xticks(range(width), alphabet[:width]) 
plt.yticks(range(height), alphabet[:height]) 
plt.savefig('confusion_matrix.png', format='png') 

Umarım yardımcı olur yukarıdaki görüntüyü oluşturmak kodudur.

43

enter image description here

Eğer plt.matshow() yerine plt.imshow() kullanabilir veya amillerrhodes yıllardan @

import seaborn as sn 
import pandas as pd 
import matplotlib.pyplot as plt 
array = [[33,2,0,0,0,0,0,0,0,1,3], 
     [3,31,0,0,0,0,0,0,0,0,0], 
     [0,4,41,0,0,0,0,0,0,0,1], 
     [0,1,0,30,0,6,0,0,0,0,1], 
     [0,0,0,0,38,10,0,0,0,0,0], 
     [0,0,0,3,1,39,0,0,0,0,4], 
     [0,2,2,0,4,1,31,0,0,0,2], 
     [0,1,0,0,0,0,0,36,0,2,0], 
     [0,0,0,0,0,0,1,5,37,5,1], 
     [3,0,0,0,0,0,0,0,0,39,0], 
     [0,0,0,0,0,0,0,0,0,0,38]] 
df_cm = pd.DataFrame(array, index = [i for i in "ABCDEFGHIJK"], 
        columns = [i for i in "ABCDEFGHIJK"]) 
plt.figure(figsize = (10,7)) 
sn.heatmap(df_cm, annot=True) 
14

@bninopaul 'ın cevabı başlayanlar burada

tamamen değil kod olabilir "kopyalama ve çalıştırmak"

import seaborn as sn 
import pandas as pd 
import matplotlib.pyplot as plt 

array = [[13,1,1,0,2,0], 
    [3,9,6,0,1,0], 
    [0,0,16,2,0,0], 
    [0,0,0,13,0,0], 
    [0,0,0,0,15,0], 
    [0,0,1,0,0,15]]   
df_cm = pd.DataFrame(array, range(6), 
        range(6)) 
#plt.figure(figsize = (10,7)) 
sn.set(font_scale=1.4)#for label size 
sn.heatmap(df_cm, annot=True,annot_kws={"size": 16})# font size 

result

olduğunu
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