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External data adapters (#5044)

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* Use external data adapters as lightweight intermediate layer between external data and DMatrix
This commit is contained in:
Rory Mitchell 2019-12-04 10:56:17 +13:00 committed by GitHub
parent f2277e7106
commit e3c34c79be
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15 changed files with 1058 additions and 593 deletions
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14
include/xgboost/data.h
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@ -465,6 +465,20 @@ class DMatrix {
*/
static DMatrix* Create(std::unique_ptr<DataSource<SparsePage>>&& source,
const std::string& cache_prefix = "");
/**
* \brief Creates a new DMatrix from an external data adapter.
*
* \tparam AdapterT Type of the adapter.
* \param adapter View onto an external data.
* \param missing Values to count as missing.
* \param nthread Number of threads for construction.
*
* \return a Created DMatrix.
*/
template <typename AdapterT>
static DMatrix* Create(AdapterT* adapter, float missing, int nthread);
/*!
* \brief Create a DMatrix by loading data from parser.
* Parser can later be deleted after the DMatrix i created.

375
src/c_api/c_api.cc
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@ -18,9 +18,8 @@
#include "c_api_error.h"
#include "../data/simple_csr_source.h"
#include "../common/math.h"
#include "../common/io.h"
#include "../common/group_data.h"
#include "../data/adapter.h"
namespace xgboost {
@ -218,37 +217,9 @@ XGB_DLL int XGDMatrixCreateFromCSREx(const size_t* indptr,
size_t nelem,
size_t num_col,
DMatrixHandle* out) {
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
API_BEGIN();
data::SimpleCSRSource& mat = *source;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.reserve(nindptr);
data_vec.reserve(nelem);
offset_vec.resize(1);
offset_vec[0] = 0;
size_t num_column = 0;
for (size_t i = 1; i < nindptr; ++i) {
for (size_t j = indptr[i - 1]; j < indptr[i]; ++j) {
if (!common::CheckNAN(data[j])) {
// automatically skip nan.
data_vec.emplace_back(Entry(indices[j], data[j]));
num_column = std::max(num_column, static_cast<size_t>(indices[j] + 1));
}
}
offset_vec.push_back(mat.page_.data.Size());
}
mat.info.num_col_ = num_column;
if (num_col > 0) {
CHECK_LE(mat.info.num_col_, num_col)
<< "num_col=" << num_col << " vs " << mat.info.num_col_;
mat.info.num_col_ = num_col;
}
mat.info.num_row_ = nindptr - 1;
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::CSRAdapter adapter(indptr, indices, data, nindptr - 1, nelem, num_col);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
API_END();
}
@ -259,361 +230,41 @@ XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
size_t nelem,
size_t num_row,
DMatrixHandle* out) {
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
API_BEGIN();
// FIXME: User should be able to control number of threads
const int nthread = omp_get_max_threads();
data::SimpleCSRSource& mat = *source;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
common::ParallelGroupBuilder<
Entry, std::remove_reference<decltype(offset_vec)>::type::value_type>
builder(&offset_vec, &data_vec);
builder.InitBudget(0, nthread);
size_t ncol = nindptr - 1; // NOLINT(*)
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < static_cast<omp_ulong>(ncol); ++i) { // NOLINT(*)
int tid = omp_get_thread_num();
for (size_t j = col_ptr[i]; j < col_ptr[i+1]; ++j) {
if (!common::CheckNAN(data[j])) {
builder.AddBudget(indices[j], tid);
}
}
}
builder.InitStorage();
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < static_cast<omp_ulong>(ncol); ++i) { // NOLINT(*)
int tid = omp_get_thread_num();
for (size_t j = col_ptr[i]; j < col_ptr[i+1]; ++j) {
if (!common::CheckNAN(data[j])) {
builder.Push(indices[j],
Entry(static_cast<bst_uint>(i), data[j]),
tid);
}
}
}
mat.info.num_row_ = mat.page_.offset.Size() - 1;
if (num_row > 0) {
CHECK_LE(mat.info.num_row_, num_row);
// provision for empty rows at the bottom of matrix
auto& offset_vec = mat.page_.offset.HostVector();
for (uint64_t i = mat.info.num_row_; i < static_cast<uint64_t>(num_row); ++i) {
offset_vec.push_back(offset_vec.back());
}
mat.info.num_row_ = num_row;
CHECK_EQ(mat.info.num_row_, offset_vec.size() - 1); // sanity check
}
mat.info.num_col_ = ncol;
mat.info.num_nonzero_ = nelem;
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::CSCAdapter adapter(col_ptr, indices, data, nindptr - 1, num_row);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
API_END();
}
XGB_DLL int XGDMatrixCreateFromMat(const bst_float* data,
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol,
bst_float missing,
xgboost::bst_ulong ncol, bst_float missing,
DMatrixHandle* out) {
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
API_BEGIN();
data::SimpleCSRSource& mat = *source;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.resize(1+nrow);
bool nan_missing = common::CheckNAN(missing);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
const bst_float* data0 = data;
// count elements for sizing data
data = data0;
for (xgboost::bst_ulong i = 0; i < nrow; ++i, data += ncol) {
xgboost::bst_ulong nelem = 0;
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[j])) {
CHECK(nan_missing)
<< "There are NAN in the matrix, however, you did not set missing=NAN";
} else {
if (nan_missing || data[j] != missing) {
++nelem;
}
}
}
offset_vec[i+1] = offset_vec[i] + nelem;
}
data_vec.resize(mat.page_.data.Size() + offset_vec.back());
data = data0;
for (xgboost::bst_ulong i = 0; i < nrow; ++i, data += ncol) {
xgboost::bst_ulong matj = 0;
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[j])) {
} else {
if (nan_missing || data[j] != missing) {
data_vec[offset_vec[i] + matj] = Entry(j, data[j]);
++matj;
}
}
}
}
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::DenseAdapter adapter(data, nrow, nrow * ncol, ncol);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, 1));
API_END();
}
template <typename T>
void PrefixSum(T *x, size_t N) {
std::vector<T> suma;
#pragma omp parallel
{
const int ithread = omp_get_thread_num();
const int nthreads = omp_get_num_threads();
#pragma omp single
{
suma.resize(nthreads+1);
suma[0] = 0;
}
T sum = 0;
T offset = 0;
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < N; i++) {
sum += x[i];
x[i] = sum;
}
suma[ithread+1] = sum;
#pragma omp barrier
for (omp_ulong i = 0; i < static_cast<omp_ulong>(ithread+1); i++) {
offset += suma[i];
}
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < N; i++) {
x[i] += offset;
}
}
}
XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol,
bst_float missing, DMatrixHandle* out,
int nthread) {
// avoid openmp unless enough data to be worth it to avoid overhead costs
if (nrow*ncol <= 10000*50) {
return(XGDMatrixCreateFromMat(data, nrow, ncol, missing, out));
}
API_BEGIN();
const int nthreadmax = std::max(omp_get_num_procs() / 2 - 1, 1);
// const int nthreadmax = omp_get_max_threads();
if (nthread <= 0) nthread=nthreadmax;
int nthread_orig = omp_get_max_threads();
omp_set_num_threads(nthread);
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
data::SimpleCSRSource& mat = *source;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
offset_vec.resize(1+nrow);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
// Check for errors in missing elements
// Count elements per row (to avoid otherwise need to copy)
bool nan_missing = common::CheckNAN(missing);
std::vector<int> badnan;
badnan.resize(nthread, 0);
#pragma omp parallel num_threads(nthread)
{
int ithread = omp_get_thread_num();
// Count elements per row
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < nrow; ++i) {
xgboost::bst_ulong nelem = 0;
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[ncol*i + j]) && !nan_missing) {
badnan[ithread] = 1;
} else if (common::CheckNAN(data[ncol * i + j])) {
} else if (nan_missing || data[ncol * i + j] != missing) {
++nelem;
}
}
offset_vec[i+1] = nelem;
}
}
// Inform about any NaNs and resize data matrix
for (int i = 0; i < nthread; i++) {
CHECK(!badnan[i]) << "There are NAN in the matrix, however, you did not set missing=NAN";
}
// do cumulative sum (to avoid otherwise need to copy)
PrefixSum(&offset_vec[0], offset_vec.size());
data_vec.resize(mat.page_.data.Size() + offset_vec.back());
// Fill data matrix (now that know size, no need for slow push_back())
#pragma omp parallel num_threads(nthread)
{
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < nrow; ++i) {
xgboost::bst_ulong matj = 0;
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
if (common::CheckNAN(data[ncol * i + j])) {
} else if (nan_missing || data[ncol * i + j] != missing) {
data_vec[offset_vec[i] + matj] =
Entry(j, data[ncol * i + j]);
++matj;
}
}
}
}
// restore omp state
omp_set_num_threads(nthread_orig);
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::DenseAdapter adapter(data, nrow, nrow * ncol, ncol);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
API_END();
}
enum class DTType : uint8_t {
kFloat32 = 0,
kFloat64 = 1,
kBool8 = 2,
kInt32 = 3,
kInt8 = 4,
kInt16 = 5,
kInt64 = 6,
kUnknown = 7
};
DTType DTGetType(std::string type_string) {
if (type_string == "float32") {
return DTType::kFloat32;
} else if (type_string == "float64") {
return DTType::kFloat64;
} else if (type_string == "bool8") {
return DTType::kBool8;
} else if (type_string == "int32") {
return DTType::kInt32;
} else if (type_string == "int8") {
return DTType::kInt8;
} else if (type_string == "int16") {
return DTType::kInt16;
} else if (type_string == "int64") {
return DTType::kInt64;
} else {
LOG(FATAL) << "Unknown data table type.";
return DTType::kUnknown;
}
}
float DTGetValue(void* column, DTType dt_type, size_t ridx) {
float missing = std::numeric_limits<float>::quiet_NaN();
switch (dt_type) {
case DTType::kFloat32: {
float val = reinterpret_cast<float*>(column)[ridx];
return std::isfinite(val) ? val : missing;
}
case DTType::kFloat64: {
double val = reinterpret_cast<double*>(column)[ridx];
return std::isfinite(val) ? static_cast<float>(val) : missing;
}
case DTType::kBool8: {
bool val = reinterpret_cast<bool*>(column)[ridx];
return static_cast<float>(val);
}
case DTType::kInt32: {
int32_t val = reinterpret_cast<int32_t*>(column)[ridx];
return val != (-2147483647 - 1) ? static_cast<float>(val) : missing;
}
case DTType::kInt8: {
int8_t val = reinterpret_cast<int8_t*>(column)[ridx];
return val != -128 ? static_cast<float>(val) : missing;
}
case DTType::kInt16: {
int16_t val = reinterpret_cast<int16_t*>(column)[ridx];
return val != -32768 ? static_cast<float>(val) : missing;
}
case DTType::kInt64: {
int64_t val = reinterpret_cast<int64_t*>(column)[ridx];
return val != -9223372036854775807 - 1 ? static_cast<float>(val)
: missing;
}
default: {
LOG(FATAL) << "Unknown data table type.";
return 0.0f;
}
}
}
XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol, DMatrixHandle* out,
int nthread) {
// avoid openmp unless enough data to be worth it to avoid overhead costs
if (nrow * ncol <= 10000 * 50) {
nthread = 1;
}
API_BEGIN();
const int nthreadmax = std::max(omp_get_num_procs() / 2 - 1, 1);
if (nthread <= 0) nthread = nthreadmax;
int nthread_orig = omp_get_max_threads();
omp_set_num_threads(nthread);
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
data::SimpleCSRSource& mat = *source;
mat.page_.offset.Resize(1 + nrow);
mat.info.num_row_ = nrow;
mat.info.num_col_ = ncol;
auto& page_offset = mat.page_.offset.HostVector();
#pragma omp parallel num_threads(nthread)
{
// Count elements per row, column by column
for (auto j = 0u; j < ncol; ++j) {
DTType dtype = DTGetType(feature_stypes[j]);
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < nrow; ++i) {
float val = DTGetValue(data[j], dtype, i);
if (!std::isnan(val)) {
page_offset[i + 1]++;
}
}
}
}
// do cumulative sum (to avoid otherwise need to copy)
PrefixSum(&page_offset[0], page_offset.size());
mat.page_.data.Resize(mat.page_.data.Size() + page_offset.back());
auto& page_data = mat.page_.data.HostVector();
// Fill data matrix (now that know size, no need for slow push_back())
std::vector<size_t> position(nrow);
#pragma omp parallel num_threads(nthread)
{
for (xgboost::bst_ulong j = 0; j < ncol; ++j) {
DTType dtype = DTGetType(feature_stypes[j]);
#pragma omp for schedule(static)
for (omp_ulong i = 0; i < nrow; ++i) {
float val = DTGetValue(data[j], dtype, i);
if (!std::isnan(val)) {
page_data[page_offset[i] + position[i]] = Entry(j, val);
position[i]++;
}
}
}
}
// restore omp state
omp_set_num_threads(nthread_orig);
mat.info.num_nonzero_ = mat.page_.data.Size();
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
data::DataTableAdapter adapter(data, feature_stypes, nrow, ncol);
*out = new std::shared_ptr<DMatrix>(
DMatrix::Create(&adapter, std::nan(""), nthread));
API_END();
}

15
src/common/group_data.h
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@ -69,25 +69,26 @@ struct ParallelGroupBuilder {
/*! \brief step 3: initialize the necessary storage */
inline void InitStorage() {
// set rptr to correct size
SizeType rptr_fill_value = rptr_.empty() ? 0 : rptr_.back();
for (std::size_t tid = 0; tid < thread_rptr_.size(); ++tid) {
if (rptr_.size() <= thread_rptr_[tid].size()) {
rptr_.resize(thread_rptr_[tid].size() + 1); // key + 1
rptr_.resize(thread_rptr_[tid].size() + 1, rptr_fill_value); // key + 1
}
}
// initialize rptr to be beginning of each segment
std::size_t start = 0;
std::size_t count = 0;
for (std::size_t i = 0; i + 1 < rptr_.size(); ++i) {
for (std::size_t tid = 0; tid < thread_rptr_.size(); ++tid) {
std::vector<SizeType> &trptr = thread_rptr_[tid];
if (i < trptr.size()) { // i^th row is assigned for this thread
std::size_t ncnt = trptr[i]; // how many entries in this row
trptr[i] = start;
start += ncnt;
std::size_t thread_count = trptr[i]; // how many entries in this row
trptr[i] = count + rptr_.back();
count += thread_count;
}
}
rptr_[i + 1] = start; // pointer accumulated from all thread
rptr_[i + 1] += count; // pointer accumulated from all thread
}
data_.resize(start);
data_.resize(rptr_.back());
}
/*!
* \brief step 4: add data to the allocated space,

488
src/data/adapter.h Normal file
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@ -0,0 +1,488 @@
/*!
* Copyright (c) 2019 by Contributors
* \file adapter.h
*/
#ifndef XGBOOST_DATA_ADAPTER_H_
#define XGBOOST_DATA_ADAPTER_H_
#include <limits>
#include <memory>
#include <string>
namespace xgboost {
namespace data {
/** External data formats should implement an adapter as below. The
* adapter provides a uniform access to data outside xgboost, allowing
* construction of DMatrix objects from a range of sources without duplicating
* code.
*
* The adapter object is an iterator that returns batches of data. Each batch
* contains a number of "lines". A line represents a set of elements from a
* sparse input matrix, normally a row in the case of a CSR matrix or a column
* for a CSC matrix. Typically in sparse matrix formats we can efficiently
* access subsets of elements at a time, but cannot efficiently lookups elements
* by random access, hence the "line" abstraction, allowing the sparse matrix to
* return subsets of elements efficiently. Individual elements are described by
* a COO tuple (row index, column index, value).
*
* This abstraction allows us to read through different sparse matrix formats
* using the same interface. In particular we can write a DMatrix constructor
* that uses the same code to construct itself from a CSR matrix, CSC matrix,
* dense matrix, csv, libsvm file, or potentially other formats. To see why this
* is necessary, imagine we have 5 external matrix formats and 5 internal
* DMatrix types where each DMatrix needs a custom constructor for each possible
* input. The number of constructors is 5*5=25. Using an abstraction over the
* input data types the number of constructors is reduced to 5, as each DMatrix
* is oblivious to the external data format. Adding a new input source is simply
* a case of implementing an adapter.
*
* Most of the below adapters do not need more than one batch as the data
* originates from an in memory source. The file adapter does require batches to
* avoid loading the entire file in memory.
*
* An important detail is empty row/column handling. Files loaded from disk do
* not provide meta information about the number of rows/columns to expect, this
* needs to be inferred during construction. Other sparse formats may specify a
* number of rows/columns, but we can encounter entirely sparse rows or columns,
* leading to disagreement between the inferred number and the meta-info
* provided. To resolve this, adapters have methods specifying the number of
* rows/columns expected, these methods may return zero where these values must
* be inferred from data. A constructed DMatrix should agree with the input
* source on numbers of rows/columns, appending empty rows if necessary.
* */
/** \brief An adapter can return this value for number of rows or columns
* indicating that this value is currently unknown and should be inferred while
* passing over the data. */
constexpr size_t kAdapterUnknownSize = std::numeric_limits<size_t >::max();
struct COOTuple {
COOTuple(size_t row_idx, size_t column_idx, float value)
: row_idx(row_idx), column_idx(column_idx), value(value) {}
size_t row_idx{0};
size_t column_idx{0};
float value{0};
};
namespace detail {
/**
* \brief Simplifies the use of DataIter when there is only one batch.
*/
template <typename DType>
class SingleBatchDataIter : dmlc::DataIter<DType> {
public:
void BeforeFirst() override { counter = 0; }
bool Next() override {
if (counter == 0) {
counter++;
return true;
}
return false;
}
private:
int counter{0};
};
/** \brief Indicates this data source cannot contain meta-info such as labels,
* weights or qid. */
class NoMetaInfo {
public:
const float* Labels() const { return nullptr; }
const float* Weights() const { return nullptr; }
const uint64_t* Qid() const { return nullptr; }
};
}; // namespace detail
class CSRAdapterBatch : public detail::NoMetaInfo {
public:
class Line {
public:
Line(size_t row_idx, size_t size, const unsigned* feature_idx,
const float* values)
: row_idx(row_idx),
size(size),
feature_idx(feature_idx),
values(values) {}
size_t Size() const { return size; }
COOTuple GetElement(size_t idx) const {
return COOTuple(row_idx, feature_idx[idx], values[idx]);
}
private:
size_t row_idx;
size_t size;
const unsigned* feature_idx;
const float* values;
};
CSRAdapterBatch(const size_t* row_ptr, const unsigned* feature_idx,
const float* values, size_t num_rows, size_t num_elements,
size_t num_features)
: row_ptr(row_ptr),
feature_idx(feature_idx),
values(values),
num_rows(num_rows),
num_elements(num_elements),
num_features(num_features) {}
const Line GetLine(size_t idx) const {
size_t begin_offset = row_ptr[idx];
size_t end_offset = row_ptr[idx + 1];
return Line(idx, end_offset - begin_offset, &feature_idx[begin_offset],
&values[begin_offset]);
}
size_t Size() const { return num_rows; }
private:
const size_t* row_ptr;
const unsigned* feature_idx;
const float* values;
size_t num_elements;
size_t num_rows;
size_t num_features;
};
class CSRAdapter : public detail::SingleBatchDataIter<CSRAdapterBatch> {
public:
CSRAdapter(const size_t* row_ptr, const unsigned* feature_idx,
const float* values, size_t num_rows, size_t num_elements,
size_t num_features)
: batch(row_ptr, feature_idx, values, num_rows, num_elements,
num_features),
num_rows(num_rows),
num_columns(num_features) {}
const CSRAdapterBatch& Value() const override { return batch; }
size_t NumRows() const { return num_rows; }
size_t NumColumns() const { return num_columns; }
private:
CSRAdapterBatch batch;
size_t num_rows;
size_t num_columns;
};
class DenseAdapterBatch : public detail::NoMetaInfo {
public:
DenseAdapterBatch(const float* values, size_t num_rows, size_t num_elements,
size_t num_features)
: num_features(num_features),
num_rows(num_rows),
num_elements(num_elements),
values(values) {}
private:
class Line {
public:
Line(const float* values, size_t size, size_t row_idx)
: row_idx(row_idx), size(size), values(values) {}
size_t Size() const { return size; }
COOTuple GetElement(size_t idx) const {
return COOTuple(row_idx, idx, values[idx]);
}
private:
size_t row_idx;
size_t size;
const float* values;
};
public:
size_t Size() const { return num_rows; }
const Line GetLine(size_t idx) const {
return Line(values + idx * num_features, num_features, idx);
}
private:
const float* values;
size_t num_elements;
size_t num_rows;
size_t num_features;
};
class DenseAdapter : public detail::SingleBatchDataIter<DenseAdapterBatch> {
public:
DenseAdapter(const float* values, size_t num_rows, size_t num_elements,
size_t num_features)
: batch(values, num_rows, num_elements, num_features),
num_rows(num_rows),
num_columns(num_features) {}
const DenseAdapterBatch& Value() const override { return batch; }
size_t NumRows() const { return num_rows; }
size_t NumColumns() const { return num_columns; }
private:
DenseAdapterBatch batch;
size_t num_rows;
size_t num_columns;
};
class CSCAdapterBatch : public detail::NoMetaInfo {
public:
CSCAdapterBatch(const size_t* col_ptr, const unsigned* row_idx,
const float* values, size_t num_features)
: col_ptr(col_ptr),
row_idx(row_idx),
values(values),
num_features(num_features) {}
private:
class Line {
public:
Line(size_t col_idx, size_t size, const unsigned* row_idx,
const float* values)
: col_idx(col_idx), size(size), row_idx(row_idx), values(values) {}
size_t Size() const { return size; }
COOTuple GetElement(size_t idx) const {
return COOTuple(row_idx[idx], col_idx, values[idx]);
}
private:
size_t col_idx;
size_t size;
const unsigned* row_idx;
const float* values;
};
public:
size_t Size() const { return num_features; }
const Line GetLine(size_t idx) const {
size_t begin_offset = col_ptr[idx];
size_t end_offset = col_ptr[idx + 1];
return Line(idx, end_offset - begin_offset, &row_idx[begin_offset],
&values[begin_offset]);
}
private:
const size_t* col_ptr;
const unsigned* row_idx;
const float* values;
size_t num_features;
};
class CSCAdapter : public detail::SingleBatchDataIter<CSCAdapterBatch> {
public:
CSCAdapter(const size_t* col_ptr, const unsigned* row_idx,
const float* values, size_t num_features, size_t num_rows)
: batch(col_ptr, row_idx, values, num_features),
num_rows(num_rows),
num_columns(num_features) {}
const CSCAdapterBatch& Value() const override { return batch; }
// JVM package sends 0 as unknown
size_t NumRows() const {
return num_rows == 0 ? kAdapterUnknownSize : num_rows;
}
size_t NumColumns() const { return num_columns; }
private:
CSCAdapterBatch batch;
size_t num_rows;
size_t num_columns;
};
class DataTableAdapterBatch : public detail::NoMetaInfo {
public:
DataTableAdapterBatch(void** data, const char** feature_stypes,
size_t num_rows, size_t num_features)
: data(data),
feature_stypes(feature_stypes),
num_features(num_features),
num_rows(num_rows) {}
private:
enum class DTType : uint8_t {
kFloat32 = 0,
kFloat64 = 1,
kBool8 = 2,
kInt32 = 3,
kInt8 = 4,
kInt16 = 5,
kInt64 = 6,
kUnknown = 7
};
DTType DTGetType(std::string type_string) const {
if (type_string == "float32") {
return DTType::kFloat32;
} else if (type_string == "float64") {
return DTType::kFloat64;
} else if (type_string == "bool8") {
return DTType::kBool8;
} else if (type_string == "int32") {
return DTType::kInt32;
} else if (type_string == "int8") {
return DTType::kInt8;
} else if (type_string == "int16") {
return DTType::kInt16;
} else if (type_string == "int64") {
return DTType::kInt64;
} else {
LOG(FATAL) << "Unknown data table type.";
return DTType::kUnknown;
}
}
class Line {
float DTGetValue(const void* column, DTType dt_type, size_t ridx) const {
float missing = std::numeric_limits<float>::quiet_NaN();
switch (dt_type) {
case DTType::kFloat32: {
float val = reinterpret_cast<const float*>(column)[ridx];
return std::isfinite(val) ? val : missing;
}
case DTType::kFloat64: {
double val = reinterpret_cast<const double*>(column)[ridx];
return std::isfinite(val) ? static_cast<float>(val) : missing;
}
case DTType::kBool8: {
bool val = reinterpret_cast<const bool*>(column)[ridx];
return static_cast<float>(val);
}
case DTType::kInt32: {
int32_t val = reinterpret_cast<const int32_t*>(column)[ridx];
return val != (-2147483647 - 1) ? static_cast<float>(val) : missing;
}
case DTType::kInt8: {
int8_t val = reinterpret_cast<const int8_t*>(column)[ridx];
return val != -128 ? static_cast<float>(val) : missing;
}
case DTType::kInt16: {
int16_t val = reinterpret_cast<const int16_t*>(column)[ridx];
return val != -32768 ? static_cast<float>(val) : missing;
}
case DTType::kInt64: {
int64_t val = reinterpret_cast<const int64_t*>(column)[ridx];
return val != -9223372036854775807 - 1 ? static_cast<float>(val)
: missing;
}
default: {
LOG(FATAL) << "Unknown data table type.";
return 0.0f;
}
}
}
public:
Line(DTType type, size_t size, size_t column_idx, const void* column)
: type(type), size(size), column_idx(column_idx), column(column) {}
size_t Size() const { return size; }
COOTuple GetElement(size_t idx) const {
return COOTuple(idx, column_idx, DTGetValue(column, type, idx));
}
private:
DTType type;
size_t size;
size_t column_idx;
const void* column;
};
public:
size_t Size() const { return num_features; }
const Line GetLine(size_t idx) const {
return Line(DTGetType(feature_stypes[idx]), num_rows, idx, data[idx]);
}
private:
void** data;
const char** feature_stypes;
size_t num_features;
size_t num_rows;
};
class DataTableAdapter
: public detail::SingleBatchDataIter<DataTableAdapterBatch> {
public:
DataTableAdapter(void** data, const char** feature_stypes, size_t num_rows,
size_t num_features)
: batch(data, feature_stypes, num_rows, num_features),
num_rows(num_rows),
num_columns(num_features) {}
const DataTableAdapterBatch& Value() const override { return batch; }
size_t NumRows() const { return num_rows; }
size_t NumColumns() const { return num_columns; }
private:
DataTableAdapterBatch batch;
size_t num_rows;
size_t num_columns;
};
class FileAdapterBatch {
public:
class Line {
public:
Line(size_t row_idx, const uint32_t* feature_idx, const float* value,
size_t size)
: row_idx(row_idx),
feature_idx(feature_idx),
value(value),
size(size) {}
size_t Size() { return size; }
COOTuple GetElement(size_t idx) {
float fvalue = value == nullptr ? 1.0f : value[idx];
return COOTuple(row_idx, feature_idx[idx], fvalue);
}
private:
size_t row_idx;
const uint32_t* feature_idx;
const float* value;
size_t size;
};
FileAdapterBatch(const dmlc::RowBlock<uint32_t>* block, size_t row_offset)
: block(block), row_offset(row_offset) {}
Line GetLine(size_t idx) const {
auto begin = block->offset[idx];
auto end = block->offset[idx + 1];
return Line(idx + row_offset, &block->index[begin], &block->value[begin],
end - begin);
}
const float* Labels() const { return block->label; }
const float* Weights() const { return block->weight; }
const uint64_t* Qid() const { return block->qid; }
size_t Size() const { return block->size; }
private:
const dmlc::RowBlock<uint32_t>* block;
size_t row_offset;
};
/** \brief FileAdapter wraps dmlc::parser to read files and provide access in a
* common interface. */
class FileAdapter : dmlc::DataIter<FileAdapterBatch> {
public:
explicit FileAdapter(dmlc::Parser<uint32_t>* parser) : parser(parser) {}
const FileAdapterBatch& Value() const override { return *batch.get(); }
void BeforeFirst() override {
batch.reset();
parser->BeforeFirst();
row_offset = 0;
}
bool Next() override {
bool next = parser->Next();
batch.reset(new FileAdapterBatch(&parser->Value(), row_offset));
row_offset += parser->Value().size;
return next;
}
// Indicates a number of rows/columns must be inferred
size_t NumRows() const { return kAdapterUnknownSize; }
size_t NumColumns() const { return kAdapterUnknownSize; }
private:
size_t row_offset{0};
std::unique_ptr<FileAdapterBatch> batch;
dmlc::Parser<uint32_t>* parser;
};
}; // namespace data
} // namespace xgboost
#endif // XGBOOST_DATA_ADAPTER_H_

30
src/data/data.cc
View File

@ -15,6 +15,7 @@
#include "../common/io.h"
#include "../common/version.h"
#include "../common/group_data.h"
#include "../data/adapter.h"
#if DMLC_ENABLE_STD_THREAD
#include "./sparse_page_source.h"
@ -207,6 +208,7 @@ DMatrix* DMatrix::Load(const std::string& uri,
LOG(CONSOLE) << "Load part of data " << partid
<< " of " << npart << " parts";
}
// legacy handling of binary data loading
if (file_format == "auto" && npart == 1) {
int magic;
@ -214,13 +216,13 @@ DMatrix* DMatrix::Load(const std::string& uri,
if (fi != nullptr) {
common::PeekableInStream is(fi.get());
if (is.PeekRead(&magic, sizeof(magic)) == sizeof(magic) &&
magic == data::SimpleCSRSource::kMagic) {
magic == data::SimpleCSRSource::kMagic) {
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
source->LoadBinary(&is);
DMatrix* dmat = DMatrix::Create(std::move(source), cache_file);
if (!silent) {
LOG(CONSOLE) << dmat->Info().num_row_ << 'x' << dmat->Info().num_col_ << " matrix with "
<< dmat->Info().num_nonzero_ << " entries loaded from " << uri;
<< dmat->Info().num_nonzero_ << " entries loaded from " << uri;
}
return dmat;
}
@ -291,9 +293,9 @@ DMatrix* DMatrix::Create(dmlc::Parser<uint32_t>* parser,
const std::string& cache_prefix,
const size_t page_size) {
if (cache_prefix.length() == 0) {
std::unique_ptr<data::SimpleCSRSource> source(new data::SimpleCSRSource());
source->CopyFrom(parser);
return DMatrix::Create(std::move(source), cache_prefix);
data::FileAdapter adapter(parser);
return DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(),
1);
} else {
#if DMLC_ENABLE_STD_THREAD
if (!data::SparsePageSource<SparsePage>::CacheExist(cache_prefix, ".row.page")) {
@ -355,9 +357,23 @@ DMatrix* DMatrix::Create(std::unique_ptr<DataSource<SparsePage>>&& source,
#endif // DMLC_ENABLE_STD_THREAD
}
}
} // namespace xgboost
namespace xgboost {
template <typename AdapterT>
DMatrix* DMatrix::Create(AdapterT* adapter, float missing, int nthread) {
return new data::SimpleDMatrix(adapter, missing, nthread);
}
template DMatrix* DMatrix::Create<data::DenseAdapter>(data::DenseAdapter* adapter,
float missing, int nthread);
template DMatrix* DMatrix::Create<data::CSRAdapter>(data::CSRAdapter* adapter,
float missing, int nthread);
template DMatrix* DMatrix::Create<data::CSCAdapter>(data::CSCAdapter* adapter,
float missing, int nthread);
template DMatrix* DMatrix::Create<data::DataTableAdapter>(
data::DataTableAdapter* adapter, float missing, int nthread);
template DMatrix* DMatrix::Create<data::FileAdapter>(data::FileAdapter* adapter,
float missing, int nthread);
SparsePage SparsePage::GetTranspose(int num_columns) const {
SparsePage transpose;
common::ParallelGroupBuilder<Entry, bst_row_t> builder(&transpose.offset.HostVector(),

64
src/data/simple_csr_source.cc
View File

@ -6,7 +6,6 @@
#include <xgboost/logging.h>
#include <xgboost/json.h>
#include <limits>
#include "simple_csr_source.h"
#include "columnar.h"
@ -26,69 +25,6 @@ void SimpleCSRSource::CopyFrom(DMatrix* src) {
}
}
void SimpleCSRSource::CopyFrom(dmlc::Parser<uint32_t>* parser) {
// use qid to get group info
const uint64_t default_max = std::numeric_limits<uint64_t>::max();
uint64_t last_group_id = default_max;
bst_uint group_size = 0;
std::vector<uint64_t> qids;
this->Clear();
while (parser->Next()) {
const dmlc::RowBlock<uint32_t>& batch = parser->Value();
if (batch.label != nullptr) {
auto& labels = info.labels_.HostVector();
labels.insert(labels.end(), batch.label, batch.label + batch.size);
}
if (batch.weight != nullptr) {
auto& weights = info.weights_.HostVector();
weights.insert(weights.end(), batch.weight, batch.weight + batch.size);
}
if (batch.qid != nullptr) {
qids.insert(qids.end(), batch.qid, batch.qid + batch.size);
// get group
for (size_t i = 0; i < batch.size; ++i) {
const uint64_t cur_group_id = batch.qid[i];
if (last_group_id == default_max || last_group_id != cur_group_id) {
info.group_ptr_.push_back(group_size);
}
last_group_id = cur_group_id;
++group_size;
}
}
// Remove the assertion on batch.index, which can be null in the case that the data in this
// batch is entirely sparse. Although it's true that this indicates a likely issue with the
// user's data workflows, passing XGBoost entirely sparse data should not cause it to fail.
// See https://github.com/dmlc/xgboost/issues/1827 for complete detail.
// CHECK(batch.index != nullptr);
// update information
this->info.num_row_ += batch.size;
// copy the data over
auto& data_vec = page_.data.HostVector();
auto& offset_vec = page_.offset.HostVector();
for (size_t i = batch.offset[0]; i < batch.offset[batch.size]; ++i) {
uint32_t index = batch.index[i];
bst_float fvalue = batch.value == nullptr ? 1.0f : batch.value[i];
data_vec.emplace_back(index, fvalue);
this->info.num_col_ = std::max(this->info.num_col_,
static_cast<uint64_t>(index + 1));
}
size_t top = page_.offset.Size();
for (size_t i = 0; i < batch.size; ++i) {
offset_vec.push_back(offset_vec[top - 1] + batch.offset[i + 1] - batch.offset[0]);
}
}
if (last_group_id != default_max) {
if (group_size > info.group_ptr_.back()) {
info.group_ptr_.push_back(group_size);
}
}
this->info.num_nonzero_ = static_cast<uint64_t>(page_.data.Size());
// Either every row has query ID or none at all
CHECK(qids.empty() || qids.size() == info.num_row_);
}
void SimpleCSRSource::LoadBinary(dmlc::Stream* fi) {
int tmagic;
CHECK(fi->Read(&tmagic, sizeof(tmagic)) == sizeof(tmagic)) << "invalid input file format";

7
src/data/simple_csr_source.h
View File

@ -45,12 +45,7 @@ class SimpleCSRSource : public DataSource<SparsePage> {
* \param src source data iter.
*/
void CopyFrom(DMatrix* src);
/*!
* \brief copy content of data from parser, also set the additional information.
* \param src source data iter.
* \param info The additional information reflected in the parser.
*/
void CopyFrom(dmlc::Parser<uint32_t>* src);
/*!
* \brief copy content of data from foreign **GPU** columnar buffer.
* \param interfaces_str JSON representation of cuda array interfaces.

120
src/data/simple_dmatrix.h
View File

@ -11,12 +11,15 @@
#include <xgboost/data.h>
#include <algorithm>
#include <cstring>
#include <memory>
#include <limits>
#include <utility>
#include <vector>
#include "simple_csr_source.h"
#include "../common/group_data.h"
#include "../common/math.h"
#include "adapter.h"
namespace xgboost {
namespace data {
@ -26,6 +29,121 @@ class SimpleDMatrix : public DMatrix {
explicit SimpleDMatrix(std::unique_ptr<DataSource<SparsePage>>&& source)
: source_(std::move(source)) {}
template <typename AdapterT>
explicit SimpleDMatrix(AdapterT* adapter, float missing, int nthread) {
// Set number of threads but keep old value so we can reset it after
const int nthreadmax = omp_get_max_threads();
if (nthread <= 0) nthread = nthreadmax;
int nthread_original = omp_get_max_threads();
omp_set_num_threads(nthread);
source_.reset(new SimpleCSRSource());
SimpleCSRSource& mat = *reinterpret_cast<SimpleCSRSource*>(source_.get());
std::vector<uint64_t> qids;
uint64_t default_max = std::numeric_limits<uint64_t>::max();
uint64_t last_group_id = default_max;
bst_uint group_size = 0;
auto& offset_vec = mat.page_.offset.HostVector();
auto& data_vec = mat.page_.data.HostVector();
uint64_t inferred_num_columns = 0;
adapter->BeforeFirst();
// Iterate over batches of input data
while (adapter->Next()) {
auto &batch = adapter->Value();
common::ParallelGroupBuilder<
Entry, std::remove_reference<decltype(offset_vec)>::type::value_type>
builder(&offset_vec, &data_vec);
builder.InitBudget(0, nthread);
// First-pass over the batch counting valid elements
size_t num_lines = batch.Size();
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < static_cast<omp_ulong>(num_lines);
++i) { // NOLINT(*)
int tid = omp_get_thread_num();
auto line = batch.GetLine(i);
for (auto j = 0ull; j < line.Size(); j++) {
auto element = line.GetElement(j);
inferred_num_columns =
std::max(inferred_num_columns,
static_cast<uint64_t>(element.column_idx + 1));
if (!common::CheckNAN(element.value) && element.value != missing) {
builder.AddBudget(element.row_idx, tid);
}
}
}
builder.InitStorage();
// Second pass over batch, placing elements in correct position
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < static_cast<omp_ulong>(num_lines);
++i) { // NOLINT(*)
int tid = omp_get_thread_num();
auto line = batch.GetLine(i);
for (auto j = 0ull; j < line.Size(); j++) {
auto element = line.GetElement(j);
if (!common::CheckNAN(element.value) && element.value != missing) {
builder.Push(element.row_idx, Entry(element.column_idx, element.value),
tid);
}
}
}
// Append meta information if available
if (batch.Labels() != nullptr) {
auto& labels = mat.info.labels_.HostVector();
labels.insert(labels.end(), batch.Labels(), batch.Labels() + batch.Size());
}
if (batch.Weights() != nullptr) {
auto& weights = mat.info.weights_.HostVector();
weights.insert(weights.end(), batch.Weights(), batch.Weights() + batch.Size());
}
if (batch.Qid() != nullptr) {
qids.insert(qids.end(), batch.Qid(), batch.Qid() + batch.Size());
// get group
for (size_t i = 0; i < batch.Size(); ++i) {
const uint64_t cur_group_id = batch.Qid()[i];
if (last_group_id == default_max || last_group_id != cur_group_id) {
mat.info.group_ptr_.push_back(group_size);
}
last_group_id = cur_group_id;
++group_size;
}
}
}
if (last_group_id != default_max) {
if (group_size > mat.info.group_ptr_.back()) {
mat.info.group_ptr_.push_back(group_size);
}
}
// Deal with empty rows/columns if necessary
if (adapter->NumColumns() == kAdapterUnknownSize) {
mat.info.num_col_ = inferred_num_columns;
} else {
mat.info.num_col_ = adapter->NumColumns();
}
// Synchronise worker columns
rabit::Allreduce<rabit::op::Max>(&mat.info.num_col_, 1);
if (adapter->NumRows() == kAdapterUnknownSize) {
mat.info.num_row_ = offset_vec.size() - 1;
} else {
if (offset_vec.empty()) {
offset_vec.emplace_back(0);
}
while (offset_vec.size() - 1 < adapter->NumRows()) {
offset_vec.emplace_back(offset_vec.back());
}
mat.info.num_row_ = adapter->NumRows();
}
mat.info.num_nonzero_ = data_vec.size();
omp_set_num_threads(nthread_original);
}
MetaInfo& Info() override;
const MetaInfo& Info() const override;

54
tests/cpp/common/test_group_data.cc Normal file
View File

@ -0,0 +1,54 @@
/*!
* Copyright 2019 by Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/data.h>
#include "../../../src/common/group_data.h"
namespace xgboost {
namespace common {
TEST(group_data, ParallelGroupBuilder) {
std::vector<size_t> offsets;
std::vector<Entry> data;
ParallelGroupBuilder<Entry, size_t> builder(&offsets, &data);
builder.InitBudget(0, 1);
// Add two rows with two elements each
builder.AddBudget(0, 0, 2);
builder.AddBudget(1, 0, 2);
builder.InitStorage();
builder.Push(0, Entry(0, 0), 0);
builder.Push(0, Entry(1, 1), 0);
builder.Push(1, Entry(0, 2), 0);
builder.Push(1, Entry(1, 3), 0);
std::vector<Entry> expected_data{
Entry(0, 0),
Entry(1, 1),
Entry(0, 2),
Entry(1, 3),
};
std::vector<size_t> expected_offsets{0, 2, 4};
EXPECT_EQ(data, expected_data);
EXPECT_EQ(offsets, expected_offsets);
// Create new builder, add one more row given already populated offsets/data
ParallelGroupBuilder<Entry, size_t> builder2(&offsets, &data);
builder2.InitBudget(0, 1);
builder2.AddBudget(2, 0, 2);
builder2.InitStorage();
builder2.Push(2, Entry(0, 4), 0);
builder2.Push(2, Entry(1, 5), 0);
expected_data.emplace_back(Entry(0, 4));
expected_data.emplace_back(Entry(1, 5));
expected_offsets.emplace_back(6);
EXPECT_EQ(data, expected_data);
EXPECT_EQ(offsets, expected_offsets);
}
} // namespace common
} // namespace xgboost

104
tests/cpp/data/test_adapter.cc Normal file
View File

@ -0,0 +1,104 @@
// Copyright (c) 2019 by Contributors
#include <gtest/gtest.h>
#include <xgboost/c_api.h>
#include <xgboost/data.h>
#include <xgboost/version_config.h>
#include "../../../src/data/adapter.h"
#include "../../../src/data/simple_dmatrix.h"
#include "../../../src/common/timer.h"
#include "../helpers.h"
using namespace xgboost; // NOLINT
TEST(c_api, CSRAdapter) {
int m = 3;
int n = 2;
std::vector<float> data = {1, 2, 3, 4, 5};
std::vector<unsigned> feature_idx = {0, 1, 0, 1, 1};
std::vector<size_t> row_ptr = {0, 2, 4, 5};
data::CSRAdapter adapter(row_ptr.data(), feature_idx.data(), data.data(),
row_ptr.size() - 1, data.size(), n);
adapter.Next();
auto & batch = adapter.Value();
auto line0 = batch.GetLine(0);
EXPECT_EQ(line0.GetElement(0).value, 1);
EXPECT_EQ(line0.GetElement(1).value, 2);
auto line1 = batch.GetLine(1);
EXPECT_EQ(line1 .GetElement(0).value, 3);
EXPECT_EQ(line1 .GetElement(1).value, 4);
auto line2 = batch.GetLine(2);
EXPECT_EQ(line2 .GetElement(0).value, 5);
EXPECT_EQ(line2 .GetElement(0).row_idx, 2);
EXPECT_EQ(line2 .GetElement(0).column_idx, 1);
data::SimpleDMatrix dmat(&adapter, -1, std::nan(""));
EXPECT_EQ(dmat.Info().num_col_, 2);
EXPECT_EQ(dmat.Info().num_row_, 3);
EXPECT_EQ(dmat.Info().num_nonzero_, 5);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
for(auto j = 0ull; j < inst.size(); j++)
{
EXPECT_EQ(inst[j].fvalue, data[row_ptr[i] + j]);
EXPECT_EQ(inst[j].index, feature_idx[row_ptr[i] + j]);
}
}
}
}
TEST(c_api, DenseAdapter) {
int m = 3;
int n = 2;
std::vector<float> data = {1, 2, 3, 4, 5, 6};
data::DenseAdapter adapter(data.data(), m, m*n, n);
data::SimpleDMatrix dmat(&adapter,-1,std::numeric_limits<float>::quiet_NaN());
EXPECT_EQ(dmat.Info().num_col_, 2);
EXPECT_EQ(dmat.Info().num_row_, 3);
EXPECT_EQ(dmat.Info().num_nonzero_, 6);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
for(auto j = 0ull; j < inst.size(); j++)
{
EXPECT_EQ(inst[j].fvalue, data[i*n+j]);
EXPECT_EQ(inst[j].index, j);
}
}
}
}
TEST(c_api, CSCAdapter) {
std::vector<float> data = {1, 3, 2, 4, 5};
std::vector<unsigned> row_idx = {0, 1, 0, 1, 2};
std::vector<size_t> col_ptr = {0, 2, 5};
data::CSCAdapter adapter(col_ptr.data(), row_idx.data(), data.data(), 2, 3);
data::SimpleDMatrix dmat(&adapter,-1,std::numeric_limits<float>::quiet_NaN());
EXPECT_EQ(dmat.Info().num_col_, 2);
EXPECT_EQ(dmat.Info().num_row_, 3);
EXPECT_EQ(dmat.Info().num_nonzero_, 5);
auto &batch = *dmat.GetBatches<SparsePage>().begin();
auto inst = batch[0];
EXPECT_EQ(inst[0].fvalue, 1);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 2);
EXPECT_EQ(inst[1].index, 1);
inst = batch[1];
EXPECT_EQ(inst[0].fvalue, 3);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 4);
EXPECT_EQ(inst[1].index, 1);
inst = batch[2];
EXPECT_EQ(inst[0].fvalue, 5);
EXPECT_EQ(inst[0].index, 1);
}
TEST(c_api, FileAdapter) {
std::string filename = "test.libsvm";
CreateBigTestData(filename, 10);
std::unique_ptr<dmlc::Parser<uint32_t>> parser(dmlc::Parser<uint32_t>::Create(filename.c_str(), 0, 1,"auto"));
data::FileAdapter adapter(parser.get());
}

1
tests/cpp/data/test_data.cc
View File

@ -101,7 +101,6 @@ TEST(DMatrix, Uri) {
std::string path = tmpdir.path + "/small.csv";
std::ofstream fout(path);
ASSERT_TRUE(fout);
size_t i = 0;
for (size_t r = 0; r < kRows; ++r) {
for (size_t c = 0; c < kCols; ++c) {

63
tests/cpp/data/test_simple_dmatrix.cc
View File

@ -4,6 +4,9 @@
#include "../../../src/data/simple_dmatrix.h"
#include "../helpers.h"
#include "../../../src/data/adapter.h"
using namespace xgboost; // NOLINT
TEST(SimpleDMatrix, MetaInfo) {
dmlc::TemporaryDirectory tempdir;
@ -63,3 +66,63 @@ TEST(SimpleDMatrix, ColAccessWithoutBatches) {
EXPECT_EQ(num_col_batch, 1) << "Expected number of batches to be 1";
delete dmat;
}
TEST(SimpleDMatrix, Empty) {
std::vector<float> data{};
std::vector<unsigned> feature_idx = {};
std::vector<size_t> row_ptr = {};
data::CSRAdapter csr_adapter(row_ptr.data(), feature_idx.data(), data.data(), 0, 0, 0);
data::SimpleDMatrix dmat(&csr_adapter,
std::numeric_limits<float>::quiet_NaN(), 1);
CHECK_EQ(dmat.Info().num_nonzero_, 0);
CHECK_EQ(dmat.Info().num_row_, 0);
CHECK_EQ(dmat.Info().num_col_, 0);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
CHECK_EQ(batch.Size(), 0);
}
data::DenseAdapter dense_adapter(nullptr, 0, 0, 0);
dmat = data::SimpleDMatrix(&dense_adapter,
std::numeric_limits<float>::quiet_NaN(), 1);
CHECK_EQ(dmat.Info().num_nonzero_, 0);
CHECK_EQ(dmat.Info().num_row_, 0);
CHECK_EQ(dmat.Info().num_col_, 0);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
CHECK_EQ(batch.Size(), 0);
}
data::CSCAdapter csc_adapter(nullptr, nullptr, nullptr, 0, 0);
dmat = data::SimpleDMatrix(&csc_adapter,
std::numeric_limits<float>::quiet_NaN(), 1);
CHECK_EQ(dmat.Info().num_nonzero_, 0);
CHECK_EQ(dmat.Info().num_row_, 0);
CHECK_EQ(dmat.Info().num_col_, 0);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
CHECK_EQ(batch.Size(), 0);
}
}
TEST(SimpleDMatrix, MissingData) {
std::vector<float> data{0.0, std::nanf(""), 1.0};
std::vector<unsigned> feature_idx = {0, 1, 0};
std::vector<size_t> row_ptr = {0, 2, 3};
data::CSRAdapter adapter(row_ptr.data(), feature_idx.data(), data.data(), 2, 3, 2);
data::SimpleDMatrix dmat(&adapter, std::numeric_limits<float>::quiet_NaN(), 1);
CHECK_EQ(dmat.Info().num_nonzero_, 2);
dmat = data::SimpleDMatrix(&adapter, 1.0, 1);
CHECK_EQ(dmat.Info().num_nonzero_, 1);
}
TEST(SimpleDMatrix, EmptyRow) {
std::vector<float> data{0.0, 1.0};
std::vector<unsigned> feature_idx = {0, 1};
std::vector<size_t> row_ptr = {0, 2, 2};
data::CSRAdapter adapter(row_ptr.data(), feature_idx.data(), data.data(), 2, 2, 2);
data::SimpleDMatrix dmat(&adapter, std::numeric_limits<float>::quiet_NaN(), 1);
CHECK_EQ(dmat.Info().num_nonzero_, 2);
CHECK_EQ(dmat.Info().num_row_, 2);
CHECK_EQ(dmat.Info().num_col_, 2);
}

112
tests/python/test_basic.py
View File

@ -64,25 +64,6 @@ class TestBasic(unittest.TestCase):
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
def test_np_view(self):
# Sliced Float32 array
y = np.array([12, 34, 56], np.float32)[::2]
from_view = xgb.DMatrix(np.array([[]]), label=y).get_label()
from_array = xgb.DMatrix(np.array([[]]), label=y + 0).get_label()
assert (from_view.shape == from_array.shape)
assert (from_view == from_array).all()
# Sliced UInt array
z = np.array([12, 34, 56], np.uint32)[::2]
dmat = xgb.DMatrix(np.array([[]]))
dmat.set_uint_info('root_index', z)
from_view = dmat.get_uint_info('root_index')
dmat = xgb.DMatrix(np.array([[]]))
dmat.set_uint_info('root_index', z + 0)
from_array = dmat.get_uint_info('root_index')
assert (from_view.shape == from_array.shape)
assert (from_view == from_array).all()
def test_record_results(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
@ -127,72 +108,6 @@ class TestBasic(unittest.TestCase):
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
def test_dmatrix_init(self):
data = np.random.randn(5, 5)
# different length
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=list('abcdef'))
# contains duplicates
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=['a', 'b', 'c', 'd', 'd'])
# contains symbol
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=['a', 'b', 'c', 'd', 'e<1'])
dm = xgb.DMatrix(data)
dm.feature_names = list('abcde')
assert dm.feature_names == list('abcde')
assert dm.slice([0, 1]).feature_names == dm.feature_names
dm.feature_types = 'q'
assert dm.feature_types == list('qqqqq')
dm.feature_types = list('qiqiq')
assert dm.feature_types == list('qiqiq')
def incorrect_type_set():
dm.feature_types = list('abcde')
self.assertRaises(ValueError, incorrect_type_set)
# reset
dm.feature_names = None
self.assertEqual(dm.feature_names, ['f0', 'f1', 'f2', 'f3', 'f4'])
assert dm.feature_types is None
def test_feature_names(self):
data = np.random.randn(100, 5)
target = np.array([0, 1] * 50)
cases = [['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'],
[u'要因1', u'要因2', u'要因3', u'要因4', u'要因5']]
for features in cases:
dm = xgb.DMatrix(data, label=target,
feature_names=features)
assert dm.feature_names == features
assert dm.num_row() == 100
assert dm.num_col() == 5
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'eta': 0.3,
'num_class': 3}
bst = xgb.train(params, dm, num_boost_round=10)
scores = bst.get_fscore()
assert list(sorted(k for k in scores)) == features
dummy = np.random.randn(5, 5)
dm = xgb.DMatrix(dummy, feature_names=features)
bst.predict(dm)
# different feature name must raises error
dm = xgb.DMatrix(dummy, feature_names=list('abcde'))
self.assertRaises(ValueError, bst.predict, dm)
def test_dump(self):
data = np.random.randn(100, 2)
target = np.array([0, 1] * 50)
@ -250,27 +165,6 @@ class TestBasic(unittest.TestCase):
assert dm.num_row() == row
assert dm.num_col() == cols
def test_dmatrix_numpy_init(self):
data = np.random.randn(5, 5)
dm = xgb.DMatrix(data)
assert dm.num_row() == 5
assert dm.num_col() == 5
data = np.array([[1, 2], [3, 4]])
dm = xgb.DMatrix(data)
assert dm.num_row() == 2
assert dm.num_col() == 2
# 0d array
self.assertRaises(ValueError, xgb.DMatrix, np.array(1))
# 1d array
self.assertRaises(ValueError, xgb.DMatrix, np.array([1, 2, 3]))
# 3d array
data = np.random.randn(5, 5, 5)
self.assertRaises(ValueError, xgb.DMatrix, data)
# object dtype
data = np.array([['a', 'b'], ['c', 'd']])
self.assertRaises(ValueError, xgb.DMatrix, data)
def test_cv(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
@ -336,12 +230,6 @@ class TestBasic(unittest.TestCase):
' dtype=float32)]')
assert output == solution
def test_get_info(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtrain.get_float_info('label')
dtrain.get_float_info('weight')
dtrain.get_float_info('base_margin')
dtrain.get_uint_info('root_index')
class TestBasicPathLike(unittest.TestCase):

171
tests/python/test_dmatrix.py Normal file
View File

@ -0,0 +1,171 @@
# -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import unittest
import scipy.sparse
from scipy.sparse import rand
rng = np.random.RandomState(1)
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestDMatrix(unittest.TestCase):
def test_dmatrix_numpy_init(self):
data = np.random.randn(5, 5)
dm = xgb.DMatrix(data)
assert dm.num_row() == 5
assert dm.num_col() == 5
data = np.array([[1, 2], [3, 4]])
dm = xgb.DMatrix(data)
assert dm.num_row() == 2
assert dm.num_col() == 2
# 0d array
self.assertRaises(ValueError, xgb.DMatrix, np.array(1))
# 1d array
self.assertRaises(ValueError, xgb.DMatrix, np.array([1, 2, 3]))
# 3d array
data = np.random.randn(5, 5, 5)
self.assertRaises(ValueError, xgb.DMatrix, data)
# object dtype
data = np.array([['a', 'b'], ['c', 'd']])
self.assertRaises(ValueError, xgb.DMatrix, data)
def test_csr(self):
indptr = np.array([0, 2, 3, 6])
indices = np.array([0, 2, 2, 0, 1, 2])
data = np.array([1, 2, 3, 4, 5, 6])
X = scipy.sparse.csr_matrix((data, indices, indptr), shape=(3, 3))
dtrain = xgb.DMatrix(X)
assert dtrain.num_row() == 3
assert dtrain.num_col() == 3
def test_csc(self):
row = np.array([0, 2, 2, 0, 1, 2])
col = np.array([0, 0, 1, 2, 2, 2])
data = np.array([1, 2, 3, 4, 5, 6])
X = scipy.sparse.csc_matrix((data, (row, col)), shape=(3, 3))
dtrain = xgb.DMatrix(X)
assert dtrain.num_row() == 3
assert dtrain.num_col() == 3
def test_np_view(self):
# Sliced Float32 array
y = np.array([12, 34, 56], np.float32)[::2]
from_view = xgb.DMatrix(np.array([[]]), label=y).get_label()
from_array = xgb.DMatrix(np.array([[]]), label=y + 0).get_label()
assert (from_view.shape == from_array.shape)
assert (from_view == from_array).all()
# Sliced UInt array
z = np.array([12, 34, 56], np.uint32)[::2]
dmat = xgb.DMatrix(np.array([[]]))
dmat.set_uint_info('root_index', z)
from_view = dmat.get_uint_info('root_index')
dmat = xgb.DMatrix(np.array([[]]))
dmat.set_uint_info('root_index', z + 0)
from_array = dmat.get_uint_info('root_index')
assert (from_view.shape == from_array.shape)
assert (from_view == from_array).all()
def test_feature_names(self):
data = np.random.randn(5, 5)
# different length
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=list('abcdef'))
# contains duplicates
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=['a', 'b', 'c', 'd', 'd'])
# contains symbol
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=['a', 'b', 'c', 'd', 'e<1'])
dm = xgb.DMatrix(data)
dm.feature_names = list('abcde')
assert dm.feature_names == list('abcde')
assert dm.slice([0, 1]).feature_names == dm.feature_names
dm.feature_types = 'q'
assert dm.feature_types == list('qqqqq')
dm.feature_types = list('qiqiq')
assert dm.feature_types == list('qiqiq')
def incorrect_type_set():
dm.feature_types = list('abcde')
self.assertRaises(ValueError, incorrect_type_set)
# reset
dm.feature_names = None
self.assertEqual(dm.feature_names, ['f0', 'f1', 'f2', 'f3', 'f4'])
assert dm.feature_types is None
def test_feature_names(self):
data = np.random.randn(100, 5)
target = np.array([0, 1] * 50)
cases = [['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'],
[u'要因1', u'要因2', u'要因3', u'要因4', u'要因5']]
for features in cases:
dm = xgb.DMatrix(data, label=target,
feature_names=features)
assert dm.feature_names == features
assert dm.num_row() == 100
assert dm.num_col() == 5
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'eta': 0.3,
'num_class': 3}
bst = xgb.train(params, dm, num_boost_round=10)
scores = bst.get_fscore()
assert list(sorted(k for k in scores)) == features
dummy = np.random.randn(5, 5)
dm = xgb.DMatrix(dummy, feature_names=features)
bst.predict(dm)
# different feature name must raises error
dm = xgb.DMatrix(dummy, feature_names=list('abcde'))
self.assertRaises(ValueError, bst.predict, dm)
def test_get_info(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtrain.get_float_info('label')
dtrain.get_float_info('weight')
dtrain.get_float_info('base_margin')
dtrain.get_uint_info('root_index')
def test_sparse_dmatrix_csr(self):
nrow = 100
ncol = 1000
x = rand(nrow, ncol, density=0.0005, format='csr', random_state=rng)
assert x.indices.max() < ncol - 1
x.data[:] = 1
dtrain = xgb.DMatrix(x, label=np.random.binomial(1, 0.3, nrow))
assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
watchlist = [(dtrain, 'train')]
param = {'max_depth': 3, 'objective': 'binary:logistic', 'verbosity': 0}
bst = xgb.train(param, dtrain, 5, watchlist)
bst.predict(dtrain)
def test_sparse_dmatrix_csc(self):
nrow = 1000
ncol = 100
x = rand(nrow, ncol, density=0.0005, format='csc', random_state=rng)
assert x.indices.max() < nrow - 1
x.data[:] = 1
dtrain = xgb.DMatrix(x, label=np.random.binomial(1, 0.3, nrow))
assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
watchlist = [(dtrain, 'train')]
param = {'max_depth': 3, 'objective': 'binary:logistic', 'verbosity': 0}
bst = xgb.train(param, dtrain, 5, watchlist)
bst.predict(dtrain)

33
tests/python/test_sparse_dmatrix.py
View File

@ -1,33 +0,0 @@
import numpy as np
import xgboost as xgb
from scipy.sparse import rand
rng = np.random.RandomState(1)
param = {'max_depth': 3, 'objective': 'binary:logistic', 'verbosity': 0}
def test_sparse_dmatrix_csr():
nrow = 100
ncol = 1000
x = rand(nrow, ncol, density=0.0005, format='csr', random_state=rng)
assert x.indices.max() < ncol - 1
x.data[:] = 1
dtrain = xgb.DMatrix(x, label=np.random.binomial(1, 0.3, nrow))
assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
watchlist = [(dtrain, 'train')]
bst = xgb.train(param, dtrain, 5, watchlist)
bst.predict(dtrain)
def test_sparse_dmatrix_csc():
nrow = 1000
ncol = 100
x = rand(nrow, ncol, density=0.0005, format='csc', random_state=rng)
assert x.indices.max() < nrow - 1
x.data[:] = 1
dtrain = xgb.DMatrix(x, label=np.random.binomial(1, 0.3, nrow))
assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
watchlist = [(dtrain, 'train')]
bst = xgb.train(param, dtrain, 5, watchlist)
bst.predict(dtrain)