bugfix for train-test ratio in statistical testing (#4134)

shogun-toolbox · Feb 4, 2018 · 0b519ee · 0b519ee
1 parent 93d06f8
commit 0b519ee
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Showing 3 changed files with 83 additions and 14 deletions.
diff --git a/src/shogun/statistical_testing/internals/DataManager.cpp b/src/shogun/statistical_testing/internals/DataManager.cpp
@@ -222,24 +222,36 @@ const bool DataManager::is_blockwise() const
 
 void DataManager::set_train_test_mode(bool on)
 {
-	train_test_mode=on;
-	if (!train_test_mode)
+	if (!on)
 	{
 		train_mode=default_train_mode;
 		train_test_ratio=default_train_test_ratio;
 		cross_validation_mode=default_cross_validation_mode;
+
+		set_train_mode(train_mode);
+		set_train_test_ratio(train_test_ratio);
+
+		train_test_mode = on;
+		REQUIRE(fetchers.size() > 0, "Features are not set!\n");
+		typedef std::unique_ptr<DataFetcher> fetcher_type;
+		std::for_each(
+		    fetchers.begin(), fetchers.end(), [this](fetcher_type& f) {
+			    f->set_train_test_mode(train_test_mode);
+		    });
 	}
-	REQUIRE(fetchers.size()>0, "Features are not set!");
-	typedef std::unique_ptr<DataFetcher> fetcher_type;
-	std::for_each(fetchers.begin(), fetchers.end(), [this, on](fetcher_type& f)
+	else
 	{
-		f->set_train_test_mode(on);
-		if (on)
-		{
-			f->set_train_mode(train_mode);
-			f->set_train_test_ratio(train_test_ratio);
-		}
-	});
+		train_test_mode = on;
+		REQUIRE(fetchers.size() > 0, "Features are not set!\n");
+		typedef std::unique_ptr<DataFetcher> fetcher_type;
+		std::for_each(
+		    fetchers.begin(), fetchers.end(), [this](fetcher_type& f) {
+			    f->set_train_test_mode(train_test_mode);
+		    });
+
+		set_train_mode(train_mode);
+		set_train_test_ratio(train_test_ratio);
+	}
 }
 
 bool DataManager::is_train_test_mode() const
@@ -250,7 +262,14 @@ bool DataManager::is_train_test_mode() const
 void DataManager::set_train_mode(bool on)
 {
 	if (train_test_mode)
+	{
 		train_mode=on;
+		REQUIRE(fetchers.size() > 0, "Features are not set!\n");
+		typedef std::unique_ptr<DataFetcher> fetcher_type;
+		std::for_each(
+		    fetchers.begin(), fetchers.end(),
+		    [this](fetcher_type& f) { f->set_train_mode(train_mode); });
+	}
 	else
 	{
 		SG_SERROR("Train mode cannot be used without turning on Train/Test mode first!"
@@ -282,7 +301,15 @@ bool DataManager::is_cross_validation_mode() const
 void DataManager::set_train_test_ratio(float64_t ratio)
 {
 	if (train_test_mode)
+	{
 		train_test_ratio=ratio;
+		REQUIRE(fetchers.size() > 0, "Features are not set!\n");
+		typedef std::unique_ptr<DataFetcher> fetcher_type;
+		std::for_each(
+		    fetchers.begin(), fetchers.end(), [this](fetcher_type& f) {
+			    f->set_train_test_ratio(train_test_ratio);
+		    });
+	}
 	else
 	{
 		SG_SERROR("Train-test ratio cannot be set without turning on Train/Test mode first!"

diff --git a/src/shogun/statistical_testing/internals/mmd/ComputeMMD.h b/src/shogun/statistical_testing/internals/mmd/ComputeMMD.h
@@ -87,7 +87,16 @@ struct ComputeMMD
 	{
 		ASSERT(m_n_x>0 && m_n_y>0);
 		const index_t size=m_n_x+m_n_y;
-		ASSERT(kernel_matrix.num_rows==size && kernel_matrix.num_cols==size);
+		REQUIRE(
+			kernel_matrix.num_rows == size,
+			"Number of rows from kernel matrix (%d) did not match the total "
+			"number of samples from both distribution (%d)\n",
+			kernel_matrix.num_rows, size);
+		REQUIRE(
+			kernel_matrix.num_cols == size,
+			"Number of cols from kernel matrix (%d) did not match the total "
+			"number of samples from both distribution (%d)\n",
+			kernel_matrix.num_cols, size);
 
 		typedef Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> MatrixXt;
 		typedef Eigen::Block<Eigen::Map<const MatrixXt> > BlockXt;

diff --git a/tests/unit/statistical_testing/KernelSelection_unittest.cc b/tests/unit/statistical_testing/KernelSelection_unittest.cc
@@ -108,6 +108,39 @@ TEST(KernelSelectionMaxMMD, quadratic_time_single_kernel_dense)
 	EXPECT_NEAR(selected_kernel->get_width(), 0.25, 1E-10);
 }
 
+TEST(
+    KernelSelectionMaxMMD,
+    quadratic_time_single_kernel_dense_unequal_train_test_ratio)
+{
+	const index_t m = 10;
+	const index_t n = 20;
+	const index_t dim = 1;
+	const float64_t difference = 0.5;
+	const index_t num_kernels = 10;
+	const float64_t train_test_ratio = 4;
+
+	sg_rand->set_seed(12345);
+
+	auto gen_p = some<CMeanShiftDataGenerator>(0, dim, 0);
+	auto gen_q = some<CMeanShiftDataGenerator>(difference, dim, 0);
+
+	auto feats_p = gen_p->get_streamed_features(m);
+	auto feats_q = gen_q->get_streamed_features(n);
+
+	auto mmd = some<CQuadraticTimeMMD>(feats_p, feats_q);
+	mmd->set_statistic_type(ST_BIASED_FULL);
+	for (auto i = 0, sigma = -5; i < num_kernels; ++i, sigma += 1)
+	{
+		float64_t tau = pow(2, sigma);
+		mmd->add_kernel(new CGaussianKernel(10, tau));
+	}
+	mmd->set_kernel_selection_strategy(KSM_MAXIMIZE_MMD);
+
+	mmd->set_train_test_mode(true);
+	mmd->set_train_test_ratio(train_test_ratio);
+	EXPECT_NO_THROW(mmd->select_kernel());
+}
+
 #ifdef USE_GPL_SHOGUN
 TEST(KernelSelectionMaxMMD, linear_time_weighted_kernel_streaming)
 {
@@ -289,7 +322,7 @@ TEST(KernelSelectionMaxCrossValidation, quadratic_time_single_kernel_dense)
 	mmd->set_train_test_mode(false);
 
 	auto selected_kernel=static_cast<CGaussianKernel*>(mmd->get_kernel());
-	EXPECT_NEAR(selected_kernel->get_width(), 0.25, 1E-10);
+	EXPECT_NEAR(selected_kernel->get_width(), 0.03125, 1E-10);
 }
 
 TEST(KernelSelectionMaxCrossValidation, linear_time_single_kernel_dense)