Визуализация матрицы

Question

Каков наилучший способ визуализации содержимого org.apache.spark.mllib.linalg.Matrix/DenseMatrix с помощью python?

Например confusionMatrix

from pyspark.mllib.linalg import Vectors, DenseMatrix 

m = DenseMatrix(3,3,[ 3.33470000e+04, 2.44000000e+02, 1.60000000e+01, 2.93500000e+03, 1.01400000e+03, 1.90000000e+01, 3.50300000e+03, 1.24000000e+02, 1.20000000e+01]) 
print(m) 
m.toArray() 

DenseMatrix([[ 3.33470000e+04, 2.93500000e+03, 3.50300000e+03], 
      [ 2.44000000e+02, 1.01400000e+03, 1.24000000e+02], 
      [ 1.60000000e+01, 1.90000000e+01, 1.20000000e+01]]) 
Out[58]: 
array([[ 3.33470000e+04, 2.93500000e+03, 3.50300000e+03], 
     [ 2.44000000e+02, 1.01400000e+03, 1.24000000e+02], 
     [ 1.60000000e+01, 1.90000000e+01, 1.20000000e+01]]) 

Answer 1

С Сиборн

from pyspark.mllib.linalg import Vectors, DenseMatrix 
import numpy as np 
import pandas as pd 

import matplotlib.pyplot as plt 
import seaborn as sns 
%matplotlib inline 
sns.set() 


    from pyspark.mllib.linalg import Vectors, DenseMatrix 
import numpy as np 

def display_cm(m): 
    a = m.toArray().astype(np.float) 
    print(a) 
    print(m) 
    row_sums = a.sum(axis=1) 
    percentage_matrix = a.astype(np.float)/row_sums[:, np.newaxis] 
    #percentage_matrix = 100 *a.astype(np.float64) /a.astype(np.float64).sum(axis=1) 
    print(percentage_matrix) 
    plt.figure(figsize=(3, 3)) 
    sns.heatmap(percentage_matrix, annot=True, fmt='.2f', xticklabels=['0' ,'1','2'], yticklabels=['0' ,'1','2']); 
    plt.title('Confusion Matrix'); 


    m = DenseMatrix(3,3,[ 3.33470000e+04,2.93500000e+03,3.50300000e+03,2.44000000e+02,1.01400000e+03, 1.24000000e+02,1.60000000e+01,1.90000000e+01,1.20000000e+01]) 


    display_cm(m)