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fixes for knn
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@Alexandr-Solovev
Alexandr-Solovev committed Oct 2, 2024
1 parent e038049 commit 314c7a4
Showing 1 changed file with 37 additions and 22 deletions.
59 changes: 37 additions & 22 deletions ...gorithms/k_nearest_neighbors/kdtree_knn_classification_predict_dense_default_batch_impl.i
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@
#include "src/algorithms/k_nearest_neighbors/kdtree_knn_classification_model_impl.h"
#include "src/algorithms/k_nearest_neighbors/kdtree_knn_impl.i"
#include "src/algorithms/k_nearest_neighbors/knn_heap.h"

#include <iostream>
namespace daal
{
namespace algorithms
Expand Down Expand Up @@ -234,17 +234,22 @@ Status KNNClassificationPredictKernel<algorithmFpType, defaultDense, cpu>::compu
if (labels)
{
const size_t yColumnCount = y->getNumberOfColumns();
std::cout << "here labels -1" << std::endl;
data_management::BlockDescriptor<algorithmFpType> yBD;
std::cout << "here labels 0" << std::endl;
y->getBlockOfRows(first, last - first, writeOnly, yBD);
auto * const dy = yBD.getBlockPtr();
std::cout << "here labels 1" << std::endl;
for (size_t i = 0; i < last - first; ++i)
{
findNearestNeighbors(&dx[i * xColumnCount], local->heap, local->stack, k, radius, kdTreeTable, rootTreeNodeIndex, data, isHomogenSOA,
soa_arrays);
DAAL_CHECK_STATUS_THR(
predict(&(dy[i * yColumnCount]), local->heap, labels, k, voteWeights, modelIndices, indicesBD, distancesBD, i, nClasses));
predict(&dy[i * yColumnCount], local->heap, labels, k, voteWeights, modelIndices, indicesBD, distancesBD, i, nClasses));
}
DAAL_CHECK_STATUS_THR(y->releaseBlockOfRows(yBD));
std::cout << "here labels 2" << std::endl;
y->releaseBlockOfRows(yBD);
std::cout << "here labels 3" << std::endl;
}
else
{
Expand All @@ -265,18 +270,22 @@ Status KNNClassificationPredictKernel<algorithmFpType, defaultDense, cpu>::compu
{
DAAL_CHECK_STATUS_THR(distances->releaseBlockOfRows(distancesBD));
}

const_cast<NumericTable &>(*x).releaseBlockOfRows(xBD);
});

std::cout << "here final 1" << std::endl;
status = safeStat.detach();
std::cout << "here final 2" << std::endl;
if (!status) return status;
std::cout << "here final 3" << std::endl;
localTLS.reduce([&](Local * ptr) -> void {
if (ptr)
{
ptr->stack.clear();
std::cout << "here final 4" << std::endl;
ptr->heap.clear();
std::cout << "here final 5" << std::endl;
service_scalable_free<Local, cpu>(ptr);
std::cout << "here final 6" << std::endl;
}
});
return status;
Expand Down Expand Up @@ -427,22 +436,23 @@ services::Status KNNClassificationPredictKernel<algorithmFpType, defaultDense, c
data_management::BlockDescriptor<algorithmFpType> & distances, size_t index, const size_t nClasses)
{
typedef daal::internal::MathInst<algorithmFpType, cpu> Math;

std::cout << "here debug1" << std::endl;
const size_t heapSize = heap.size();
if (heapSize < 1) return services::Status();

std::cout << "here debug2" << std::endl;
if (indices.getNumberOfRows() != 0)
{
std::cout << "here debug3" << std::endl;
DAAL_ASSERT(modelIndices);

services::Status s;
data_management::BlockDescriptor<int> modelIndicesBD;

std::cout << "here debug4" << std::endl;
const auto nIndices = indices.getNumberOfColumns();
DAAL_ASSERT(heapSize <= nIndices);

int * const indicesPtr = indices.getBlockPtr() + index * nIndices;

std::cout << "here debug5" << std::endl;
for (size_t i = 0; i < heapSize; ++i)
{
s |= const_cast<NumericTable *>(modelIndices)->getBlockOfRows(heap[i].index, 1, readOnly, modelIndicesBD);
Expand All @@ -453,51 +463,53 @@ services::Status KNNClassificationPredictKernel<algorithmFpType, defaultDense, c
s |= const_cast<NumericTable *>(modelIndices)->releaseBlockOfRows(modelIndicesBD);
DAAL_ASSERT(s.ok());
}
std::cout << "here debug6" << std::endl;
}

if (distances.getNumberOfRows() != 0)
{
services::Status s;

std::cout << "here debug7" << std::endl;
const auto nDistances = distances.getNumberOfColumns();
DAAL_ASSERT(heapSize <= nDistances);

std::cout << "here debug8" << std::endl;
algorithmFpType * const distancesPtr = distances.getBlockPtr() + index * nDistances;
for (size_t i = 0; i < heapSize; ++i)
{
distancesPtr[i] = heap[i].distance;
}

std::cout << "here debug9" << std::endl;
Math::xvSqrt(heapSize, distancesPtr, distancesPtr);

std::cout << "here debug10" << std::endl;
for (size_t i = heapSize; i < nDistances; ++i)
{
distancesPtr[i] = -1;
}
std::cout << "here debug11" << std::endl;
}

if (labels)
{
DAAL_ASSERT(predictedClass);

std::cout << "here debug12" << std::endl;
data_management::BlockDescriptor<algorithmFpType> labelBD;
algorithmFpType * classes = static_cast<algorithmFpType *>(daal::services::internal::service_malloc<algorithmFpType, cpu>(heapSize));
algorithmFpType * classWeights = static_cast<algorithmFpType *>(daal::services::internal::service_malloc<algorithmFpType, cpu>(nClasses));
DAAL_CHECK_MALLOC(classWeights);
DAAL_CHECK_MALLOC(classes);

std::cout << "here debug13" << std::endl;
for (size_t i = 0; i < nClasses; ++i)
{
classWeights[i] = 0;
}

std::cout << "here debug14" << std::endl;
for (size_t i = 0; i < heapSize; ++i)
{
const_cast<NumericTable *>(labels)->getBlockOfColumnValues(0, heap[i].index, 1, readOnly, labelBD);
classes[i] = *(labelBD.getBlockPtr());
const_cast<NumericTable *>(labels)->releaseBlockOfColumnValues(labelBD);
}

std::cout << "here debug15" << std::endl;
if (voteWeights == voteUniform)
{
for (size_t i = 0; i < heapSize; ++i)
Expand All @@ -508,11 +520,11 @@ services::Status KNNClassificationPredictKernel<algorithmFpType, defaultDense, c
else
{
DAAL_ASSERT(voteWeights == voteDistance);

std::cout << "here debug17" << std::endl;
const algorithmFpType epsilon = daal::services::internal::EpsilonVal<algorithmFpType>::get();

bool isContainZero = false;

std::cout << "here debug18" << std::endl;
for (size_t i = 0; i < heapSize; ++i)
{
if (heap[i].distance <= epsilon)
Expand All @@ -521,7 +533,7 @@ services::Status KNNClassificationPredictKernel<algorithmFpType, defaultDense, c
break;
}
}

std::cout << "here debug19" << std::endl;
if (isContainZero)
{
for (size_t i = 0; i < heapSize; ++i)
Expand All @@ -540,7 +552,7 @@ services::Status KNNClassificationPredictKernel<algorithmFpType, defaultDense, c
}
}
}

std::cout << "here debug21" << std::endl;
algorithmFpType maxWeightClass = 0;
algorithmFpType maxWeight = 0;
for (size_t i = 0; i < nClasses; ++i)
Expand All @@ -551,10 +563,13 @@ services::Status KNNClassificationPredictKernel<algorithmFpType, defaultDense, c
maxWeightClass = i;
}
}
std::cout << "here debug22" << std::endl;
*predictedClass = maxWeightClass;

std::cout << "here debug23" << std::endl;
service_free<algorithmFpType, cpu>(classes);
std::cout << "here debug24" << std::endl;
service_free<algorithmFpType, cpu>(classWeights);
std::cout << "here debug25" << std::endl;
classes = nullptr;
}

Expand Down

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