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What is datasets dump_svmlight_file() in sklearn?

Salman Yousaf

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This dump_svmlight_file() method is used to dump the dataset in libsvmA simple and efficient software solution for SVM classification and regression./svmlight file format. It is a text-based format with one sample per line. It is valuable for sparse datasets because it does not store zero valued features.

_svmlight_ is an implementation of Vapnik’s Support Vector Machine (SVM) written in C.

Syntax


sklearn.datasets.dump_svmlight_file(
  X,
  y,
  f,
  *,
  zero_based= True,
  comment= None,
  query_id= None,
  multilabel= False
 )

Parameters

  • X: This represents the array of a sample with the desired features and forms a sparse matrix. n samples and n features represent the number of samples and features.
  • Y: This represents the array, but with the target values stored in it. Its labels must have the float or integer datatype.
  • f: This is like binary mode, and it shows us the track where our data is stored. If the data is stored in this format, then it will return the output in binary form.
  • zero_based: This is a Boolean that will show us whether our program is according to the conditions and if it is true or false. It will show an output value of zero for true, and non-zero for false.
  • comment: The default for this parameter is none. It will be on top of the program. All the elements of comment are in the SVMlight fil3, and it must be a Unicode string that can deal with ASCII code.
  • query_idarray: The default for this parameter is none. It will contain the conditions in pair form, and also in an array, but using the same format as a comment.
  • multilabel: The default for this parameter is none. It will contain many labels according to the demand of the program.
import numpy as np
# data vectors
Y1 = [[1, 7, 7], [8, 5, 3], [0, 1, 2]]
# numpy ararys
Y1_as_array = np.array(Y1)
print(Y1_as_array)
# dump_svmlight_file (multilabel)
def dump_tst_ml():
X1 = [[9, 0, 9, 0, 2],
[0, 1, 0, 0, 1],
[2, 5, 1, 6, 0]]
ff = BytesIO()
dump_svmlight_file(X1, Y1, ff, multilabel=True)
ff.seek(0)
# it must be assured that multilabel is dumped accurately
asrt_eql(ff.readline(), b("1 0:1 2:3 4:5\n"))
asrt_eql(ff.readline(), b("0,2 \n"))
asrt_eql(ff.readline(), b("0,1 1:5 3:1\n"))

RELATED TAGS

datasets
sklearn
c
communitycreator
python

Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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