diff --git a/src/shogun/statistical_testing/internals/DataFetcher.cpp b/src/shogun/statistical_testing/internals/DataFetcher.cpp
index b60c14555bd..f870ab8bd30 100644
--- a/src/shogun/statistical_testing/internals/DataFetcher.cpp
+++ b/src/shogun/statistical_testing/internals/DataFetcher.cpp
@@ -50,6 +50,7 @@ const char* DataFetcher::get_name() const
 void DataFetcher::set_train_test_ratio(float64_t train_test_ratio)
 {
 	m_num_samples=m_train_test_details.get_total_num_samples();
+	REQUIRE(m_num_samples>0, "Number of samples is not set!\n");
 	index_t num_training_samples=m_num_samples*train_test_ratio/(train_test_ratio+1);
 	m_train_test_details.set_num_training_samples(num_training_samples);
 	SG_SINFO("Must set the train/test mode by calling set_train_mode(True/False)!\n");
@@ -68,10 +69,12 @@ void DataFetcher::set_train_mode(bool train_mode)
 		m_num_samples=m_train_test_details.get_num_training_samples();
 		if (m_num_samples==0)
 			SG_SERROR("The number of training samples is 0! Please set a valid train-test ratio\n");
+		SG_SINFO("Using %d number of samples for training!\n", m_num_samples);
 	}
 	else
 	{
 		m_num_samples=m_train_test_details.get_num_test_samples();
+		SG_SINFO("Using %d number of samples for testing!\n", m_num_samples);
 		start_index=m_train_test_details.get_num_training_samples();
 		if (start_index==0)
 		{
diff --git a/src/shogun/statistical_testing/internals/DataFetcher.h b/src/shogun/statistical_testing/internals/DataFetcher.h
index 40c675552b5..4e8c9849d80 100644
--- a/src/shogun/statistical_testing/internals/DataFetcher.h
+++ b/src/shogun/statistical_testing/internals/DataFetcher.h
@@ -52,9 +52,9 @@ class DataFetcher
 public:
 	DataFetcher(CFeatures* samples);
 	virtual ~DataFetcher();
-	void set_train_test_ratio(float64_t train_test_ratio);
+	virtual void set_train_test_ratio(float64_t train_test_ratio);
 	float64_t get_train_test_ratio() const;
-	void set_train_mode(bool train_mode);
+	virtual void set_train_mode(bool train_mode);
 	void set_xvalidation_mode(bool xvalidation_mode);
 	index_t get_num_folds() const;
 	void use_fold(index_t idx);
diff --git a/src/shogun/statistical_testing/internals/StreamingDataFetcher.cpp b/src/shogun/statistical_testing/internals/StreamingDataFetcher.cpp
index a811120cb91..37457b94c8a 100644
--- a/src/shogun/statistical_testing/internals/StreamingDataFetcher.cpp
+++ b/src/shogun/statistical_testing/internals/StreamingDataFetcher.cpp
@@ -48,14 +48,38 @@ const char* StreamingDataFetcher::get_name() const
 void StreamingDataFetcher::set_num_samples(index_t num_samples)
 {
 	m_num_samples=num_samples;
+	m_train_test_details.set_total_num_samples(m_num_samples);
+}
+
+void StreamingDataFetcher::set_train_test_ratio(float64_t train_test_ratio)
+{
+	if (m_train_test_details.get_total_num_samples()==0)
+		m_train_test_details.set_total_num_samples(m_num_samples);
+	DataFetcher::set_train_test_ratio(train_test_ratio);
+}
+
+void StreamingDataFetcher::set_train_mode(bool train_mode)
+{
+	if (train_mode)
+	{
+		m_num_samples=m_train_test_details.get_num_training_samples();
+		if (m_num_samples==0)
+			SG_SERROR("The number of training samples is 0! Please set a valid train-test ratio\n");
+		SG_SINFO("Using %d number of samples for training!\n", m_num_samples);
+	}
+	else
+	{
+		m_num_samples=m_train_test_details.get_num_test_samples();
+		SG_SINFO("Using %d number of samples for testing!\n", m_num_samples);
+	}
 }
 
 void StreamingDataFetcher::start()
 {
 	REQUIRE(m_num_samples>0, "Number of samples is not set! It is MANDATORY for streaming features!\n");
-	if (m_block_details.m_blocksize==0)
+	if (m_block_details.m_full_data || m_block_details.m_blocksize>m_num_samples)
 	{
-		SG_SINFO("Block details not set! Fetching entire data (%d samples)!\n", m_num_samples);
+		SG_SINFO("Fetching entire data (%d samples)!\n", m_num_samples);
 		m_block_details.with_blocksize(m_num_samples);
 	}
 	m_block_details.m_total_num_blocks=m_num_samples/m_block_details.m_blocksize;
@@ -64,7 +88,6 @@ void StreamingDataFetcher::start()
 	{
 		m_samples->start_parser();
 		parser_running=true;
-		// TODO check if resetting the stream is required
 	}
 }
 
diff --git a/src/shogun/statistical_testing/internals/StreamingDataFetcher.h b/src/shogun/statistical_testing/internals/StreamingDataFetcher.h
index b5fcf7fdfce..91941b0f2ef 100644
--- a/src/shogun/statistical_testing/internals/StreamingDataFetcher.h
+++ b/src/shogun/statistical_testing/internals/StreamingDataFetcher.h
@@ -39,6 +39,8 @@ class StreamingDataFetcher : public DataFetcher
 public:
 	StreamingDataFetcher(CStreamingFeatures* samples);
 	virtual ~StreamingDataFetcher() override;
+	virtual void set_train_test_ratio(float64_t train_test_ratio) override;
+	virtual void set_train_mode(bool train_mode) override;
 	virtual void start() override;
 	virtual CFeatures* next() override;
 	virtual void reset() override;
diff --git a/tests/unit/statistical_testing/internals/DataManager_unittest.cc b/tests/unit/statistical_testing/internals/DataManager_unittest.cc
index 0cf7b1af4e9..31920f531f0 100644
--- a/tests/unit/statistical_testing/internals/DataManager_unittest.cc
+++ b/tests/unit/statistical_testing/internals/DataManager_unittest.cc
@@ -674,3 +674,204 @@ TEST(DataManager, train_data_two_distributions_normal_feats_blockwise)
 	ASSERT_TRUE(total==num_vec);
 	mgr.end();
 }
+
+TEST(DataManager, train_data_two_distributions_streaming_feats)
+{
+	const index_t dim=3;
+	const index_t num_vec=8;
+	const index_t num_distributions=2;
+	const index_t train_test_ratio=3;
+
+	SGMatrix<float64_t> data_p(dim, num_vec);
+	std::iota(data_p.matrix, data_p.matrix+dim*num_vec, 0);
+
+	SGMatrix<float64_t> data_q(dim, num_vec);
+	std::iota(data_q.matrix, data_q.matrix+dim*num_vec, dim*num_vec);
+
+	using feat_type=CDenseFeatures<float64_t>;
+	auto feats_p=new feat_type(data_p);
+	auto feats_q=new feat_type(data_q);
+
+	DataManager mgr(num_distributions);
+	mgr.samples_at(0)=new CStreamingDenseFeatures<float64_t>(feats_p);
+	mgr.samples_at(1)=new CStreamingDenseFeatures<float64_t>(feats_q);
+	mgr.num_samples_at(0)=num_vec;
+	mgr.num_samples_at(1)=num_vec;
+
+	// training data
+	mgr.set_train_test_ratio(train_test_ratio);
+	mgr.set_train_mode(true);
+	mgr.start();
+
+	auto next_burst=mgr.next();
+	ASSERT_TRUE(!next_burst.empty());
+	ASSERT_TRUE(next_burst.num_blocks()==1);
+
+	auto tmp_p=dynamic_cast<feat_type*>(next_burst[0][0].get());
+	auto tmp_q=dynamic_cast<feat_type*>(next_burst[1][0].get());
+
+	ASSERT_TRUE(tmp_p!=nullptr);
+	ASSERT_TRUE(tmp_q!=nullptr);
+	ASSERT_TRUE(tmp_p->get_num_vectors()==num_vec*train_test_ratio/(train_test_ratio+1));
+	ASSERT_TRUE(tmp_q->get_num_vectors()==num_vec*train_test_ratio/(train_test_ratio+1));
+
+	next_burst=mgr.next();
+	ASSERT_TRUE(next_burst.empty());
+	mgr.end();
+
+	// test data
+	mgr.set_train_mode(false);
+	mgr.start();
+
+	next_burst=mgr.next();
+	ASSERT_TRUE(!next_burst.empty());
+	ASSERT_TRUE(next_burst.num_blocks()==1);
+
+	tmp_p=dynamic_cast<feat_type*>(next_burst[0][0].get());
+	tmp_q=dynamic_cast<feat_type*>(next_burst[1][0].get());
+
+	ASSERT_TRUE(tmp_p!=nullptr);
+	ASSERT_TRUE(tmp_q!=nullptr);
+	ASSERT_TRUE(tmp_p->get_num_vectors()==num_vec/(train_test_ratio+1));
+	ASSERT_TRUE(tmp_q->get_num_vectors()==num_vec/(train_test_ratio+1));
+
+	next_burst=mgr.next();
+	ASSERT_TRUE(next_burst.empty());
+	mgr.end();
+
+	// full data
+	mgr.set_train_test_ratio(0);
+	mgr.set_train_mode(false);
+	mgr.reset();
+	mgr.start();
+
+	next_burst=mgr.next();
+	ASSERT_TRUE(!next_burst.empty());
+	ASSERT_TRUE(next_burst.num_blocks()==1);
+
+	tmp_p=dynamic_cast<feat_type*>(next_burst[0][0].get());
+	tmp_q=dynamic_cast<feat_type*>(next_burst[1][0].get());
+
+	ASSERT_TRUE(tmp_p!=nullptr);
+	ASSERT_TRUE(tmp_q!=nullptr);
+	ASSERT_TRUE(tmp_p->get_num_vectors()==num_vec);
+	ASSERT_TRUE(tmp_q->get_num_vectors()==num_vec);
+
+	next_burst=mgr.next();
+	ASSERT_TRUE(next_burst.empty());
+	mgr.end();
+}
+
+TEST(DataManager, train_data_two_distributions_streaming_feats_blockwise)
+{
+	const index_t dim=3;
+	const index_t num_vec=8;
+	const index_t blocksize=2;
+	const index_t num_blocks_per_burst=2;
+	const index_t num_distributions=2;
+	const index_t train_test_ratio=3;
+
+	SGMatrix<float64_t> data_p(dim, num_vec);
+	std::iota(data_p.matrix, data_p.matrix+dim*num_vec, 0);
+
+	SGMatrix<float64_t> data_q(dim, num_vec);
+	std::iota(data_q.matrix, data_q.matrix+dim*num_vec, dim*num_vec);
+
+	using feat_type=CDenseFeatures<float64_t>;
+	auto feats_p=new feat_type(data_p);
+	auto feats_q=new feat_type(data_q);
+
+	DataManager mgr(num_distributions);
+	mgr.samples_at(0)=new CStreamingDenseFeatures<float64_t>(feats_p);
+	mgr.samples_at(1)=new CStreamingDenseFeatures<float64_t>(feats_q);
+	mgr.num_samples_at(0)=num_vec;
+	mgr.num_samples_at(1)=num_vec;
+
+	mgr.set_blocksize(blocksize);
+	mgr.set_num_blocks_per_burst(num_blocks_per_burst);
+
+	mgr.set_train_test_ratio(train_test_ratio);
+
+	// train data
+	mgr.set_train_mode(true);
+	mgr.start();
+
+	auto next_burst=mgr.next();
+	ASSERT_TRUE(!next_burst.empty());
+
+	auto total=0;
+
+	while (!next_burst.empty())
+	{
+		ASSERT_TRUE(next_burst.num_blocks()==num_blocks_per_burst);
+		for (auto i=0; i<next_burst.num_blocks(); ++i)
+		{
+			auto tmp_p=dynamic_cast<feat_type*>(next_burst[0][i].get());
+			auto tmp_q=dynamic_cast<feat_type*>(next_burst[1][i].get());
+			ASSERT_TRUE(tmp_p!=nullptr);
+			ASSERT_TRUE(tmp_q!=nullptr);
+			ASSERT_TRUE(tmp_p->get_num_vectors()==blocksize/2);
+			ASSERT_TRUE(tmp_q->get_num_vectors()==blocksize/2);
+			total+=tmp_p->get_num_vectors();
+		}
+		next_burst=mgr.next();
+	}
+	ASSERT_TRUE(total==num_vec*train_test_ratio/(train_test_ratio+1));
+	mgr.end();
+
+	// test data
+	mgr.set_train_mode(false);
+	mgr.start();
+
+	next_burst=mgr.next();
+	ASSERT_TRUE(!next_burst.empty());
+
+	total=0;
+
+	while (!next_burst.empty())
+	{
+		ASSERT_TRUE(next_burst.num_blocks()==num_blocks_per_burst);
+		for (auto i=0; i<next_burst.num_blocks(); ++i)
+		{
+			auto tmp_p=dynamic_cast<feat_type*>(next_burst[0][i].get());
+			auto tmp_q=dynamic_cast<feat_type*>(next_burst[1][i].get());
+			ASSERT_TRUE(tmp_p!=nullptr);
+			ASSERT_TRUE(tmp_q!=nullptr);
+			ASSERT_TRUE(tmp_p->get_num_vectors()==blocksize/2);
+			ASSERT_TRUE(tmp_q->get_num_vectors()==blocksize/2);
+			total+=tmp_p->get_num_vectors();
+		}
+		next_burst=mgr.next();
+	}
+	ASSERT_TRUE(total==num_vec/(train_test_ratio+1));
+	mgr.end();
+
+	// full data
+	mgr.set_train_test_ratio(0);
+	mgr.set_train_mode(false);
+	mgr.reset();
+	mgr.start();
+
+	next_burst=mgr.next();
+	ASSERT_TRUE(!next_burst.empty());
+
+	total=0;
+
+	while (!next_burst.empty())
+	{
+		ASSERT_TRUE(next_burst.num_blocks()==num_blocks_per_burst);
+		for (auto i=0; i<next_burst.num_blocks(); ++i)
+		{
+			auto tmp_p=dynamic_cast<feat_type*>(next_burst[0][i].get());
+			auto tmp_q=dynamic_cast<feat_type*>(next_burst[1][i].get());
+			ASSERT_TRUE(tmp_p!=nullptr);
+			ASSERT_TRUE(tmp_q!=nullptr);
+			ASSERT_TRUE(tmp_p->get_num_vectors()==blocksize/2);
+			ASSERT_TRUE(tmp_q->get_num_vectors()==blocksize/2);
+			total+=tmp_p->get_num_vectors();
+		}
+		next_burst=mgr.next();
+	}
+	ASSERT_TRUE(total==num_vec);
+	mgr.end();
+}