Added subset support for multitask algorithms

shogun-toolbox · Jul 27, 2012 · bea8e08 · bea8e08
1 parent 57d6371
commit bea8e08
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Showing 10 changed files with 78 additions and 28 deletions.
diff --git a/src/shogun/features/Features.cpp b/src/shogun/features/Features.cpp
@@ -366,6 +366,11 @@ void CFeatures::remove_all_subsets()
 	subset_changed_post();
 }
 
+CSubsetStack* CFeatures::get_subset_stack()
+{
+	return m_subset_stack;
+}
+
 CFeatures* CFeatures::copy_subset(SGVector<index_t> indices)
 {
 	SG_ERROR("%s::copy_subset(): copy_subset and therefore model storage of "

diff --git a/src/shogun/features/Features.h b/src/shogun/features/Features.h
@@ -252,6 +252,8 @@ class CFeatures : public CSGObject
 		 * Calls subset_changed_post() afterwards */
 		virtual void remove_all_subsets();
 
+		virtual CSubsetStack* get_subset_stack();
+
 		/** method may be overwritten to update things that depend on subset */
 		virtual void subset_changed_post() {}
 

diff --git a/src/shogun/features/Subset.h b/src/shogun/features/Subset.h
@@ -43,6 +43,9 @@ class CSubset: public CSGObject
 	/** @return name of the SGSerializable */
 	inline const char* get_name() const { return "Subset"; }
 
+	/** get subset indices */
+	SGVector<index_t> get_subset_idx() const { return m_subset_idx; }
+
 private:
 	void init();
 

diff --git a/src/shogun/features/SubsetStack.h b/src/shogun/features/SubsetStack.h
@@ -70,6 +70,8 @@ class CSubsetStack: public CSGObject
 		return m_active_subsets_stack->get_num_elements();
 	}
 
+	CSubset* get_last_subset() const { return m_active_subset; }
+
 	/** returns the corresponding real index of a subset index
 	 * Maps through all added subsets in stack.
 	 *

diff --git a/src/shogun/lib/malsar/malsar_low_rank.cpp b/src/shogun/lib/malsar/malsar_low_rank.cpp
@@ -38,22 +38,6 @@ malsar_result_t malsar_low_rank(
 	// initialize weight vector and bias for each task
 	MatrixXd Ws = MatrixXd::Zero(n_feats, n_tasks);
 	VectorXd Cs = VectorXd::Zero(n_tasks);
-	for (task=0; task<n_tasks; task++)
-	{
-		int n_pos = 0;
-		int n_neg = 0;
-		SGVector<index_t> task_idx = options.tasks_indices[task];
-		for (int i=0; i<task_idx.vlen; i++)
-		{
-			if (y[task_idx[i]] > 0)
-				n_pos++;
-			else
-				n_neg++;
-		}
-		SG_SDEBUG("There are %d positive and %d negative examples in task %d\n",n_pos,n_neg,task);
-		Cs[task] = CMath::log(double(n_pos)/n_neg);
-	}
-
 	MatrixXd Wz=Ws, Wzp=Ws, Wz_old=Ws, delta_Wzp=Ws, gWs=Ws;
 	VectorXd Cz=Cs, Czp=Cs, Cz_old=Cs, delta_Czp=Cs, gCs=Cs;
 
@@ -131,7 +115,7 @@ malsar_result_t malsar_low_rank(
 				int n_task_vecs = task_idx.vlen;
 				for (int i=0; i<n_task_vecs; i++)
 				{
-					double aa = -y[task_idx[i]]*(features->dense_dot(task_idx[i], Wzp.col(task).data(), n_feats)+Cs[task]);
+					double aa = -y[task_idx[i]]*(features->dense_dot(task_idx[i], Wzp.col(task).data(), n_feats)+Czp[task]);
 					double bb = CMath::max(aa,0.0);
 
 					Fzp += (CMath::log(CMath::exp(-bb) + CMath::exp(aa-bb)) + bb)/n_task_vecs;
@@ -181,6 +165,7 @@ malsar_result_t malsar_low_rank(
 		obj += rho*svd.singularValues().sum();
 		internal::set_is_malloc_allowed(false);
 
+
 		// check if process should be terminated 
 		switch (options.termination)
 		{

diff --git a/src/shogun/transfer/multitask/MultitaskClusteredLogisticRegression.cpp b/src/shogun/transfer/multitask/MultitaskClusteredLogisticRegression.cpp
@@ -71,6 +71,7 @@ bool CMultitaskClusteredLogisticRegression::train_machine(CFeatures* data)
 
 	ASSERT(features);
 	ASSERT(m_labels);
+	ASSERT(m_task_relation);
 
 	SGVector<float64_t> y(m_labels->get_num_labels());
 	for (int32_t i=0; i<y.vlen; i++)
@@ -83,6 +84,8 @@ bool CMultitaskClusteredLogisticRegression::train_machine(CFeatures* data)
 	options.n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
 	options.tasks_indices = ((CTaskGroup*)m_task_relation)->get_tasks_indices();
 	options.n_clusters = m_num_clusters;
+	SGVector<index_t>* subset_tasks_indices = get_subset_tasks_indices();
+	options.tasks_indices = subset_tasks_indices;
 
 #ifdef HAVE_EIGEN3
 	malsar_result_t model = malsar_clustered(
@@ -95,8 +98,10 @@ bool CMultitaskClusteredLogisticRegression::train_machine(CFeatures* data)
 	m_tasks_w = SGMatrix<float64_t>(((CDotFeatures*)features)->get_dim_feature_space(), options.n_tasks); 
 	m_tasks_c = SGVector<float64_t>(options.n_tasks); 
 #endif
-
-	ASSERT(m_task_relation);
+
+	for (int32_t i=0; i<options.n_tasks; i++)
+		subset_tasks_indices[i].~SGVector<index_t>();
+	SG_FREE(subset_tasks_indices);
 
 	return true;
 }

diff --git a/src/shogun/transfer/multitask/MultitaskL1L2LogisticRegression.cpp b/src/shogun/transfer/multitask/MultitaskL1L2LogisticRegression.cpp
@@ -50,6 +50,7 @@ bool CMultitaskL1L2LogisticRegression::train_machine(CFeatures* data)
 
 	ASSERT(features);
 	ASSERT(m_labels);
+	ASSERT(m_task_relation);
 
 	SGVector<float64_t> y(m_labels->get_num_labels());
 	for (int32_t i=0; i<y.vlen; i++)
@@ -60,7 +61,8 @@ bool CMultitaskL1L2LogisticRegression::train_machine(CFeatures* data)
 	options.tolerance = m_tolerance;
 	options.max_iter = m_max_iter;
 	options.n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
-	options.tasks_indices = ((CTaskGroup*)m_task_relation)->get_tasks_indices();
+	SGVector<index_t>* subset_tasks_indices = get_subset_tasks_indices();
+	options.tasks_indices = subset_tasks_indices;
 
 #ifdef HAVE_EIGEN3
 	malsar_result_t model = malsar_joint_feature_learning(
@@ -73,9 +75,11 @@ bool CMultitaskL1L2LogisticRegression::train_machine(CFeatures* data)
 	m_tasks_w = SGMatrix<float64_t>(((CDotFeatures*)features)->get_dim_feature_space(), options.n_tasks); 
 	m_tasks_c = SGVector<float64_t>(options.n_tasks); 
 #endif
-
-	ASSERT(m_task_relation);
 
+	for (int32_t i=0; i<options.n_tasks; i++)
+		subset_tasks_indices[i].~SGVector<index_t>();
+	SG_FREE(subset_tasks_indices);
+
 	return true;
 }
 

diff --git a/src/shogun/transfer/multitask/MultitaskLogisticRegression.cpp b/src/shogun/transfer/multitask/MultitaskLogisticRegression.cpp
@@ -11,6 +11,11 @@
 #include <shogun/lib/slep/slep_solver.h>
 #include <shogun/lib/slep/slep_options.h>
 
+#include <map>
+#include <vector>
+
+using namespace std;
+
 namespace shogun
 {
 
@@ -137,4 +142,40 @@ bool CMultitaskLogisticRegression::train_machine(CFeatures* data)
 	return true;
 }
 
+SGVector<index_t>* CMultitaskLogisticRegression::get_subset_tasks_indices()
+{
+	int n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
+	SGVector<index_t>* tasks_indices = ((CTaskGroup*)m_task_relation)->get_tasks_indices();
+
+	CSubsetStack* sstack = features->get_subset_stack();
+	map<index_t,index_t> subset_inv_map = map<index_t,index_t>();
+	for (int32_t i=0; i<sstack->get_size(); i++)
+		subset_inv_map[sstack->subset_idx_conversion(i)] = i;
+
+	SGVector<index_t>* subset_tasks_indices = SG_MALLOC(SGVector<index_t>, n_tasks);
+	for (int32_t i=0; i<n_tasks; i++)
+	{
+		new (&subset_tasks_indices[i]) SGVector<index_t>();
+		SGVector<index_t> task = tasks_indices[i];
+		//task.display_vector("task");
+		vector<index_t> cutted = vector<index_t>();
+		for (int32_t j=0; j<task.vlen; j++)
+		{
+			if (subset_inv_map.count(task[j]))
+				cutted.push_back(subset_inv_map[task[j]]);
+		}
+		SGVector<index_t> cutted_task(cutted.size());
+		for (int32_t j=0; j<cutted_task.vlen; j++)
+			cutted_task[j] = cutted[j];
+		//cutted_task.display_vector("cutted");
+		subset_tasks_indices[i] = cutted_task;
+	}
+	for (int32_t i=0; i<n_tasks; i++)
+		tasks_indices[i].~SGVector<index_t>();
+	SG_FREE(tasks_indices);
+
+	return subset_tasks_indices;
+}
+
+
 }
diff --git a/src/shogun/transfer/multitask/MultitaskLogisticRegression.h b/src/shogun/transfer/multitask/MultitaskLogisticRegression.h
@@ -74,6 +74,9 @@ class CMultitaskLogisticRegression : public CSLEPMachine
 		/** train machine */
 		virtual bool train_machine(CFeatures* data=NULL);
 
+		/** subset mapped task indices */
+		SGVector<index_t>* get_subset_tasks_indices();
+
 	private:
 
 		/** register parameters */

diff --git a/src/shogun/transfer/multitask/MultitaskTraceLogisticRegression.cpp b/src/shogun/transfer/multitask/MultitaskTraceLogisticRegression.cpp
@@ -45,6 +45,7 @@ bool CMultitaskTraceLogisticRegression::train_machine(CFeatures* data)
 
 	ASSERT(features);
 	ASSERT(m_labels);
+	ASSERT(m_task_relation);
 
 	SGVector<float64_t> y(m_labels->get_num_labels());
 	for (int32_t i=0; i<y.vlen; i++)
@@ -56,11 +57,8 @@ bool CMultitaskTraceLogisticRegression::train_machine(CFeatures* data)
 	options.max_iter = m_max_iter;
 	options.n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
 	options.tasks_indices = ((CTaskGroup*)m_task_relation)->get_tasks_indices();
-
-	SG_DEBUG("Starting malsar solver\n");
-	SG_DEBUG("N tasks = %d \n", options.n_tasks);
-	for (int32_t i=0; i<options.n_tasks; i++)
-		options.tasks_indices[i].display_vector();
+	SGVector<index_t>* subset_tasks_indices = get_subset_tasks_indices();
+	options.tasks_indices = subset_tasks_indices;
 
 #ifdef HAVE_EIGEN3
 	malsar_result_t model = malsar_low_rank(
@@ -74,7 +72,9 @@ bool CMultitaskTraceLogisticRegression::train_machine(CFeatures* data)
 	m_tasks_c = SGVector<float64_t>(options.n_tasks); 
 #endif
 
-	ASSERT(m_task_relation);
+	for (int32_t i=0; i<options.n_tasks; i++)
+		subset_tasks_indices[i].~SGVector<index_t>();
+	SG_FREE(subset_tasks_indices);
 
 	return true;
 }