CODEWITHSUNDEEP
pythondatasetlibsvm
Jose Ramon
Jose Ramon

Reputation: 5386

How to load dataset in libsvm python

I am wandering how can I load my dataset, in order to read it from libsvm python implementation. My data is a 250X500 matrix and the first column is dataset labels. I am using the following code in order to read the data:

with open("dataset3.txt") as textFile:
lines = [line.split() for line in textFile]

Matrix = [[0 for x in xrange(len(lines[0]))] for x in xrange(len(lines))] 

for y in range(0, len(lines)):
    for x in range(0, len(lines[0])):
        Matrix[y][x] = lines[y][x]

With the code above I read my data into a 2d array of int. How can I use this array in order to perform svm train and evaluation??

param = svm_parameter('-t 0 -c 4 -b 1')
m = svm_train(Matrix, param)

Text file: 

1   0   9   0   0   0   0   5   2   5   15   2   3   50   0   4   6   27   0   16   34   0   11   30   12   23   41   1   0   2   0   10   67   34 ...
-1   0   10   0   0   0   0   1   0   2   5   1   8   14   0   12   11   4   2   4   22   0   6   40   8   20   47   2   1   0   0   2   1   21   0   1   11   1  ...
...

Matrix = []
with open('dataset3.txt') as f:
row = []
for line in f:
    data = line.split()
    target = float(data[0]) # target value
    str1 = str(target) 
    for i,j in enumerate(data):
        if i==0:
            continue
        else:   
            str1 = str1 + " " + str(i) +":"+ str(j) +" " 
row.append(str1)

Upvotes: 0

Views: 3809

Answers (1)

emesday
emesday

Reputation: 6186

Try this code

with open('dataset3.txt') as f:
    Matrix = [map(float, line.split()) for line in f]
  • for line in f reads each line.
  • line.split() splits into each value
  • map(float, line.split()) converts value to float

Updated

OP commented different input format.

Matrix = []
with open('dataset3.txt') as f:
    for line in f:
        data = line.split()
        target = float(data[0]) # target value
        row = []

        for i, (idx, value) in enumerate([item.split(':') for item in data[1:]]):
            n = int(idx) - (i + 1) # num missing
            for _ in range(n):
                row.append(0) # for missing
            row.append(float(value))
        Matrix.append(row)

Upvotes: 1

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