All geek questions in one place

Is numpy.argmax slower than MATLAB [~, idx] = max ()?

I am writing a Bayesian classifier for a normal distribution. I have code in python and MATLAB that are almost identical. However, MATLAB code is about 50 times faster than my Python script. I'm new to Python, so maybe I did something terrible. I guess this is somewhere where I loop through a dataset.

Perhaps numpy.argmax () is much slower than [~, idx] = max ()? Penetration through a data frame is slow? Poor use of dictionaries (I tried the object before, and it was even slow)?

Any advice is appreciated.

Python code

import numpy as np
import pandas as pd

#import the data as a data frame
train_df = pd.read_table('hw1_traindata.txt',header = None)#training
train_df.columns = [1, 2] #rename column titles

The data here are 2 columns (300 rows / samples for training and 300,000 for test). These are function parameters; mi and Si are patterns and covariances.

case3_p = {'w': [], 'w0': [], 'W': []}
case3_p['w']={1:S1.I*m1,2:S2.I*m2,3:S3.I*m3}
case3_p['w0']={1: -1.0/2.0*(m1.T*S1.I*m1)-

1.0/2.0*np.log(np.linalg.det(S1)),
            2: -1.0/2.0*(m2.T*S2.I*m2)-1.0/2.0*np.log(np.linalg.det(S2)),
            3: -1.0/2.0*(m3.T*S3.I*m3)-1.0/2.0*np.log(np.linalg.det(S3))}
case3_p['W']={1: -1.0/2.0*S1.I,
           2: -1.0/2.0*S2.I,
           3: -1.0/2.0*S3.I}
#W1=-1.0/2.0*S1.I
#w1_3=S1.I*m1
#w01_3=-1.0/2.0*(m1.T*S1.I*m1)-1.0/2.0*np.log(np.linalg.det(S1))    
def g3(x,W,w,w0):
    return x.T*W*x+w.T*x+w0

This is a classifier / loop

train_df['case3'] = 0

for i in range(train_df.shape[0]):
    x = np.mat(train_df.loc[i,[1, 2]]).T#observation

    #case 3    
    vals = [g3(x,case3_p['W'][1],case3_p['w'][1],case3_p['w0'][1]),
            g3(x,case3_p['W'][2],case3_p['w'][2],case3_p['w0'][2]),
            g3(x,case3_p['W'][3],case3_p['w'][3],case3_p['w0'][3])]
    train_df.loc[i,'case3'] = np.argmax(vals) + 1 #add one to make it the class value

MATLAB

train = load('hw1_traindata.txt');


W1=-1/2*S1^-1;%there isn't one for the other cases
w1_3=S1^-1*m1';%fix the transpose thing
w10_3=-1/2*(m1*S1^-1*m1')-1/2*log(det(S1));
g1_3=@(x) x'*W1*x+w1_3'*x+w10_3';

W2=-1/2*S2^-1;
w2_3=S2^-1*m2';
w20_3=-1/2*(m2*S2^-1*m2')-1/2*log(det(S2));
g2_3=@(x) x'*W2*x+w2_3'*x+w20_3';

W3=-1/2*S3^-1;
w3_3=S3^-1*m3';
w30_3=-1/2*(m3*S3^-1*m3')-1/2*log(det(S3));
g3_3=@(x) x'*W3*x+w3_3'*x+w30_3';


case3_class_tr = Inf(size(act_class_tr));
for i=1:length(train)
    x=train(i,:)';%current sample

    %case3
    vals = [g1_3(x),g2_3(x),g3_3(x)];%compute discriminant function value
    [~, case3_class_tr(i)] = max(vals);%get location of max

end
+4
2

. :

import numpy as np
import pandas as pd

fname = 'hw1_traindata.txt'
ar = np.random.rand(1000, 2)
np.savetxt(fname, ar, delimiter='\t')

m1, m2, m3 = [np.mat(ar).T for ar in np.random.rand(3, 2)]
S1, S2, S3 = [np.mat(ar) for ar in np.random.rand(3, 2, 2)]

IPython lprun (line_profiler). :

%lprun -f train train(fname, m1, S1, m2, S2, m3, S3)
Timer unit: 5.59946e-07 s

Total time: 4.77361 s
File: <ipython-input-164-563f57dadab3>
Function: train at line 1

Line #   Hits     Time  Per Hit  %Time  Line Contents
=====================================================
     1                                 def train(fname, m1, S1, m2, S2, m3, S3):
     2      1     9868   9868.0   0.1      train_df = pd.read_table(fname ,header = None)#training
     3      1      328    328.0   0.0      train_df.columns = [1, 2] #rename column titles
     4                                 
     5      1       17     17.0   0.0      case3_p = {'w': [], 'w0': [], 'W': []}
     6      1      877    877.0   0.0      case3_p['w']={1:S1.I*m1,2:S2.I*m2,3:S3.I*m3}
     7      1      356    356.0   0.0      case3_p['w0']={1: -1.0/2.0*(m1.T*S1.I*m1)-
     8                                 
     9      1      204    204.0   0.0      1.0/2.0*np.log(np.linalg.det(S1)),
    10      1      498    498.0   0.0                  2: -1.0/2.0*(m2.T*S2.I*m2)-1.0/2.0*np.log(np.linalg.det(S2)),
    11      1      502    502.0   0.0                  3: -1.0/2.0*(m3.T*S3.I*m3)-1.0/2.0*np.log(np.linalg.det(S3))}
    12      1      235    235.0   0.0      case3_p['W']={1: -1.0/2.0*S1.I,
    13      1      229    229.0   0.0                 2: -1.0/2.0*S2.I,
    14      1      230    230.0   0.0                 3: -1.0/2.0*S3.I}
    15                                 
    16      1     1818   1818.0   0.0      train_df['case3'] = 0
    17                                 
    18   1001    17409     17.4   0.2      for i in range(train_df.shape[0]):
    19   1000  4254511   4254.5  49.9          x = np.mat(train_df.loc[i,[1, 2]]).T#observation
    20                                 
    21                                         #case 3    
    22   1000   298245    298.2   3.5          vals = [g3(x,case3_p['W'][1],case3_p['w'][1],case3_p['w0'][1]),
    23   1000   269825    269.8   3.2                  g3(x,case3_p['W'][2],case3_p['w'][2],case3_p['w0'][2]),
    24   1000   274279    274.3   3.2                  g3(x,case3_p['W'][3],case3_p['w'][3],case3_p['w0'][3])]
    25   1000  3395654   3395.7  39.8          train_df.loc[i,'case3'] = np.argmax(vals) + 1
    26                                 
    27      1       45     45.0   0.0      return train_df

, 90% . :

%lprun -f train train(fname, m1, S1, m2, S2, m3, S3)
Timer unit: 5.59946e-07 s

Total time: 6.15358 s
File: <ipython-input-197-92d9866b57dc>
Function: train at line 1

Line #   Hits      Time  Per Hit  %Time  Line Contents
======================================================
...     
    19   1000   5292988   5293.0   48.2          thing = train_df.loc[i,[1, 2]]  # Observation
    20   1000    265101    265.1    2.4          x = np.mat(thing).T
...     
    26   1000    143142    143.1    1.3          index = np.argmax(vals) + 1  # Add one to make it the class value
    27   1000   4164122   4164.1   37.9          train_df.loc[i,'case3'] = index

Pandas! argmax 1,5% .

, train_df['case3'] .iloc:

%lprun -f train train(fname, m1, S1, m2, S2, m3, S3)
Timer unit: 5.59946e-07 s

Total time: 3.26716 s
File: <ipython-input-192-f6173cdf9990>
Function: train at line 1

Line #   Hits      Time  Per Hit  %Time  Line Contents
======= ======= ======================================
    16      1      1548   1548.0    0.0      train_df['case3'] = np.zeros(len(train_df))
...             
    19   1000   2608489   2608.5   44.7          thing = train_df.iloc[i,[0, 1]]  # Observation
    20   1000    228959    229.0    3.9          x = np.mat(thing).T
...             
    26   1000    123165    123.2    2.1          index = np.argmax(vals) + 1  # Add one to make it the class value
    27   1000   1849283   1849.3   31.7          train_df.iloc[i,2] = index

, Pandas - . Pandas ( ), "" Numpy. . train_data = pd.read_table(fname, header=None).values. , Pandas.

:

  • Python .
  • Numpy . , , , .
  • MATLAB JIT-, Python , MATLAB .
+5

, Matlab , Numpy. , Math Kernel Library

50x , Numpy vs Matlab MKL.

Python, MKL, Enthought Anaconda

Anaconda MKL Optimizations , Anaconda , MKL. , , , .

-1

Source: https://habr.com/ru/post/1608398/

More articles:


All Articles