add data partition for libsvm iter

src/io/iter_libsvm.cc

@@ -23,6 +23,10 @@ struct LibSVMIterParam : public dmlc::Parameter<LibSVMIterParam> {
   std::string label_libsvm;
   /*! \brief label shape */
   TShape label_shape;
+  /*! \brief partition the data into multiple parts */
+  int num_parts;
+  /*! \brief the index of the part will read*/
+  int part_index;
   // declare parameters
   DMLC_DECLARE_PARAMETER(LibSVMIterParam) {
     DMLC_DECLARE_FIELD(data_libsvm)
@@ -35,6 +39,10 @@ struct LibSVMIterParam : public dmlc::Parameter<LibSVMIterParam> {
     index_t shape1[] = {1};
     DMLC_DECLARE_FIELD(label_shape).set_default(TShape(shape1, shape1 + 1))
         .describe("The shape of one label.");
+    DMLC_DECLARE_FIELD(num_parts).set_default(1)
+        .describe("partition the data into multiple parts");
+    DMLC_DECLARE_FIELD(part_index).set_default(0)
+        .describe("the index of the part will read");
   }
 };
 
@@ -47,11 +55,15 @@ class LibSVMIter: public SparseIIterator<DataInst> {
   virtual void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) {
     param_.InitAllowUnknown(kwargs);
     CHECK_EQ(param_.data_shape.ndim(), 1) << "dimension of data_shape is expected to be 1";
+    CHECK_GT(param_.num_parts, 0) << "number of parts should be positive";
+    CHECK_GE(param_.part_index, 0) << "part index should be non-negative";
     data_parser_.reset(dmlc::Parser<uint64_t>::Create(param_.data_libsvm.c_str(),
-                                                      0, 1, "libsvm"));
+                                                      param_.part_index,
+                                                      param_.num_parts, "libsvm"));
     if (param_.label_libsvm != "NULL") {
       label_parser_.reset(dmlc::Parser<uint64_t>::Create(param_.label_libsvm.c_str(),
-                                                         0, 1, "libsvm"));
+                                                         param_.part_index,
+                                                         param_.num_parts, "libsvm"));
       CHECK_GT(param_.label_shape.Size(), 1)
         << "label_shape is not expected to be (1,) when param_.label_libsvm is set.";
     } else {

tests/python/unittest/test_io.py

@@ -161,29 +161,37 @@ def check_libSVMIter_synthetic():
             assert_almost_equal(data_train.getdata().asnumpy(), expected)
             i += 1
 
-    def check_libSVMIter_news_metadata():
+    def check_libSVMIter_news_data():
         news_metadata = {
             'name': 'news20.t',
             'origin_name': 'news20.t.bz2',
             'url': "http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/news20.t.bz2",
-            'shape': 62060,
+            'feature_dim': 62060,
             'num_classes': 20,
+            'num_examples': 3993,
         }
+        num_parts = 3
+        batch_size = 128
+        num_examples = news_metadata['num_examples']
         data_dir = os.path.join(os.getcwd(), 'data')
         get_data(data_dir, news_metadata['name'], news_metadata['url'],
                  news_metadata['origin_name'])
         path = os.path.join(data_dir, news_metadata['name'])
-        data_train = mx.io.LibSVMIter(data_libsvm=path,
-                                      data_shape=(news_metadata['shape'], ),
-                                      batch_size=512)
+        data_train = mx.io.LibSVMIter(data_libsvm=path, data_shape=(news_metadata['feature_dim'],),
+                                      batch_size=batch_size, num_parts=num_parts, part_index=0)
+        num_batches = 0
         iterator = iter(data_train)
         for batch in iterator:
             # check the range of labels
             assert(np.sum(batch.label[0].asnumpy() > 20) == 0)
             assert(np.sum(batch.label[0].asnumpy() <= 0) == 0)
+            num_batches += 1
+        import math
+        expected_num_batches = math.ceil(num_examples * 1.0 / batch_size / num_parts)
+        assert(num_batches == int(expected_num_batches)), (num_batches, expected_num_batches)
 
     check_libSVMIter_synthetic()
-    check_libSVMIter_news_metadata()
+    check_libSVMIter_news_data()
 
 if __name__ == "__main__":
     test_NDArrayIter()