# mx.io.LibSVMIter¶

## Description¶

Returns the LibSVM iterator which returns data with csr storage type. This iterator is experimental and should be used with care.

The input data is stored in a format similar to LibSVM file format, except that the indices are expected to be zero-based instead of one-based, and the column indices for each row are expected to be sorted in ascending order. Details of the LibSVM format are available here.

The data_shape parameter is used to set the shape of each line of the data. The dimension of both data_shape and label_shape are expected to be 1.

The data_libsvm parameter is used to set the path input LibSVM file. When it is set to a directory, all the files in the directory will be read.

When label_libsvm is set to NULL, both data and label are read from the file specified by data_libsvm. In this case, the data is stored in csr storage type, while the label is a 1D dense array.

The LibSVMIter only support round_batch parameter set to True. Therefore, if batch_size is 3 and there are 4 total rows in libsvm file, 2 more examples are consumed at the first round.

When num_parts and part_index are provided, the data is split into num_parts partitions, and the iterator only reads the part_index-th partition. However, the partitions are not guaranteed to be even.

reset() is expected to be called only after a complete pass of data.

Example:

# Contents of libsvm file data.t.
1.0 0:0.5 2:1.2
-2.0
-3.0 0:0.6 1:2.4 2:1.2
4 2:-1.2

# Creates a LibSVMIter with batch_size=3.
>>> data_iter = mx.io.LibSVMIter(data_libsvm = 'data.t', data_shape = (3,), batch_size = 3)
# The data of the first batch is stored in csr storage type
>>> batch = data_iter.next()
>>> csr = batch.data[0]
<CSRNDArray 3x3 @cpu(0)>
>>> csr.asnumpy()
[[ 0.5        0.          1.2 ]
[ 0.          0.          0.  ]
[ 0.6         2.4         1.2]]
# The label of first batch
>>> label = batch.label[0]
>>> label
[ 1. -2. -3.]
<NDArray 3 @cpu(0)>

>>> second_batch = data_iter.next()
# The data of the second batch
>>> second_batch.data[0].asnumpy()
[[ 0.          0.         -1.2 ]
[ 0.5         0.          1.2 ]
[ 0.          0.          0. ]]
# The label of the second batch
>>> second_batch.label[0].asnumpy()
[ 4.  1. -2.]

>>> data_iter.reset()
# To restart the iterator for the second pass of the data

When label_libsvm is set to the path to another LibSVM file,
data is read from data_libsvm and label from label_libsvm.
In this case, both data and label are stored in the csr format.
If the label column in the data_libsvm file is ignored.


Example:

# Contents of libsvm file label.t
1.0
-2.0 0:0.125
-3.0 2:1.2
4 1:1.0 2:-1.2

# Creates a LibSVMIter with specified label file
>>> data_iter = mx.io.LibSVMIter(data_libsvm = 'data.t', data_shape = (3,),
label_libsvm = 'label.t', label_shape = (3,), batch_size = 3)

# Both data and label are in csr storage type
>>> batch = data_iter.next()
>>> csr_data = batch.data[0]
<CSRNDArray 3x3 @cpu(0)>
>>> csr_data.asnumpy()
[[ 0.5         0.          1.2  ]
[ 0.          0.          0.   ]
[ 0.6         2.4         1.2 ]]
>>> csr_label = batch.label[0]
<CSRNDArray 3x3 @cpu(0)>
>>> csr_label.asnumpy()
[[ 0.          0.          0.   ]
[ 0.125       0.          0.   ]
[ 0.          0.          1.2 ]]


## Usage¶

mx.io.LibSVMIter(...)


## Arguments¶

Argument

Description

data.libsvm

string, required.

The input zero-base indexed LibSVM data file or a directory path.

data.shape

Shape(tuple), required.

The shape of one example.

label.libsvm

string, optional, default=’NULL’.

The input LibSVM label file or a directory path. If NULL, all labels will be read from data_libsvm.

label.shape

Shape(tuple), optional, default=[1].

The shape of one label.

num.parts

int, optional, default=’1’.

partition the data into multiple parts

part.index

int, optional, default=’0’.

the index of the part will read

batch.size

int (non-negative), required.

Batch size.

round.batch

boolean, optional, default=1.

Whether to use round robin to handle overflow batch or not.

prefetch.buffer

long (non-negative), optional, default=4.

Maximum number of batches to prefetch.

ctx

{‘cpu’, ‘gpu’},optional, default=’gpu’.

dtype
Output data type. None means no change.
iter The result mx.dataiter