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xgboost/demo/c-api/basic/c-api-demo.c
Jiaming Yuan c1786849e3
Use array interface for CSC matrix. (#8672) 
* Use array interface for CSC matrix.

Use array interface for CSC matrix and align the interface with CSR and dense.

- Fix nthread issue in the R package DMatrix.
- Unify the behavior of handling `missing` with other inputs.
- Unify the behavior of handling `missing` around R, Python, Java, and Scala DMatrix.
- Expose `num_non_missing` to the JVM interface.
- Deprecate old CSR and CSC constructors.
2023-02-05 01:59:46 +08:00

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/**
* Copyright 2019-2023 by XGBoost contributors
*
* \file c-api-demo.c
* \brief A simple example of using xgboost C API.
*/
#include <assert.h>
#include <stddef.h>
#include <stdint.h> /* uint32_t,uint64_t */
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <xgboost/c_api.h>
#define safe_xgboost(call) { \
int err = (call); \
if (err != 0) { \
fprintf(stderr, "%s:%d: error in %s: %s\n", __FILE__, __LINE__, #call, XGBGetLastError()); \
exit(1); \
} \
}
/* Make Json encoded array interface. */
static void MakeArrayInterface(size_t data, size_t n, char const* typestr, size_t length,
char* out) {
static char const kTemplate[] =
"{\"data\": [%lu, true], \"shape\": [%lu, %lu], \"typestr\": \"%s\", \"version\": 3}";
memset(out, '\0', length);
sprintf(out, kTemplate, data, n, 1ul, typestr);
}
/* Make Json encoded DMatrix configuration. */
static void MakeConfig(int n_threads, size_t length, char* out) {
static char const kTemplate[] = "{\"missing\": NaN, \"nthread\": %d}";
memset(out, '\0', length);
sprintf(out, kTemplate, n_threads);
}
int main() {
int silent = 0;
int use_gpu = 0; // set to 1 to use the GPU for training
// load the data
DMatrixHandle dtrain, dtest;
safe_xgboost(XGDMatrixCreateFromFile("../../data/agaricus.txt.train", silent, &dtrain));
safe_xgboost(XGDMatrixCreateFromFile("../../data/agaricus.txt.test", silent, &dtest));
// create the booster
BoosterHandle booster;
DMatrixHandle eval_dmats[2] = {dtrain, dtest};
safe_xgboost(XGBoosterCreate(eval_dmats, 2, &booster));
// configure the training
// available parameters are described here:
// https://xgboost.readthedocs.io/en/latest/parameter.html
safe_xgboost(XGBoosterSetParam(booster, "tree_method", use_gpu ? "gpu_hist" : "hist"));
if (use_gpu) {
// set the GPU to use;
// this is not necessary, but provided here as an illustration
safe_xgboost(XGBoosterSetParam(booster, "gpu_id", "0"));
} else {
// avoid evaluating objective and metric on a GPU
safe_xgboost(XGBoosterSetParam(booster, "gpu_id", "-1"));
}
safe_xgboost(XGBoosterSetParam(booster, "objective", "binary:logistic"));
safe_xgboost(XGBoosterSetParam(booster, "min_child_weight", "1"));
safe_xgboost(XGBoosterSetParam(booster, "gamma", "0.1"));
safe_xgboost(XGBoosterSetParam(booster, "max_depth", "3"));
safe_xgboost(XGBoosterSetParam(booster, "verbosity", silent ? "0" : "1"));
// train and evaluate for 10 iterations
int n_trees = 10;
const char* eval_names[2] = {"train", "test"};
const char* eval_result = NULL;
for (int i = 0; i < n_trees; ++i) {
safe_xgboost(XGBoosterUpdateOneIter(booster, i, dtrain));
safe_xgboost(XGBoosterEvalOneIter(booster, i, eval_dmats, eval_names, 2, &eval_result));
printf("%s\n", eval_result);
}
bst_ulong num_feature = 0;
safe_xgboost(XGBoosterGetNumFeature(booster, &num_feature));
printf("num_feature: %lu\n", (unsigned long)(num_feature));
// predict
bst_ulong out_len = 0;
int n_print = 10;
/* Run prediction with DMatrix object. */
char const config[] =
"{\"training\": false, \"type\": 0, "
"\"iteration_begin\": 0, \"iteration_end\": 0, \"strict_shape\": false}";
/* Shape of output prediction */
uint64_t const* out_shape;
/* Dimension of output prediction */
uint64_t out_dim;
/* Pointer to a thread local contigious array, assigned in prediction function. */
float const* out_result = NULL;
safe_xgboost(
XGBoosterPredictFromDMatrix(booster, dtest, config, &out_shape, &out_dim, &out_result));
printf("y_pred: ");
for (int i = 0; i < n_print; ++i) {
printf("%1.4f ", out_result[i]);
}
printf("\n");
// print true labels
safe_xgboost(XGDMatrixGetFloatInfo(dtest, "label", &out_len, &out_result));
printf("y_test: ");
for (int i = 0; i < n_print; ++i) {
printf("%1.4f ", out_result[i]);
}
printf("\n");
{
printf("Dense Matrix Example (XGDMatrixCreateFromMat): ");
const float values[] = {0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1, 0, 0, 0, 0};
DMatrixHandle dmat;
safe_xgboost(XGDMatrixCreateFromMat(values, 1, 127, 0.0, &dmat));
const float* out_result = NULL;
safe_xgboost(
XGBoosterPredictFromDMatrix(booster, dmat, config, &out_shape, &out_dim, &out_result));
assert(out_dim == 1);
assert(out_shape[0] == 1);
printf("%1.4f \n", out_result[0]);
safe_xgboost(XGDMatrixFree(dmat));
}
{
printf("Sparse Matrix Example (XGDMatrixCreateFromCSR): ");
const uint64_t indptr[] = {0, 22};
const uint32_t indices[] = {1, 9, 19, 21, 24, 34, 36, 39, 42, 53, 56,
65, 69, 77, 86, 88, 92, 95, 102, 106, 117, 122};
const float data[] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
DMatrixHandle dmat;
char j_indptr[128];
MakeArrayInterface((size_t)indptr, 2ul, "<u8", sizeof(j_indptr), j_indptr);
char j_indices[128];
MakeArrayInterface((size_t)indices, sizeof(indices) / sizeof(uint32_t), "<u4",
sizeof(j_indices), j_indices);
char j_data[128];
MakeArrayInterface((size_t)data, sizeof(data) / sizeof(float), "<f4", sizeof(j_data), j_data);
char j_config[64];
MakeConfig(0, sizeof(j_config), j_config);
safe_xgboost(XGDMatrixCreateFromCSR(j_indptr, j_indices, j_data, 127, j_config, &dmat));
const float* out_result = NULL;
safe_xgboost(
XGBoosterPredictFromDMatrix(booster, dmat, config, &out_shape, &out_dim, &out_result));
assert(out_dim == 1);
assert(out_shape[0] == 1);
printf("%1.4f \n", out_result[0]);
safe_xgboost(XGDMatrixFree(dmat));
}
{
printf("Sparse Matrix Example (XGDMatrixCreateFromCSC): ");
const uint64_t indptr[] = {
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 7, 7, 7, 8, 8, 8, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11,
12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15,
15, 16, 16, 16, 16, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22};
const uint32_t indices[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
const float data[] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
char j_indptr[128];
MakeArrayInterface((size_t)indptr, 128ul, "<u8", sizeof(j_indptr), j_indptr);
char j_indices[128];
MakeArrayInterface((size_t)indices, sizeof(indices) / sizeof(unsigned), "<u4",
sizeof(j_indices), j_indices);
char j_data[128];
MakeArrayInterface((size_t)data, sizeof(data) / sizeof(float), "<f4", sizeof(j_data), j_data);
char j_config[64];
MakeConfig(0, sizeof(j_config), j_config);
DMatrixHandle dmat;
safe_xgboost(XGDMatrixCreateFromCSC(j_indptr, j_indices, j_data, 1, j_config, &dmat));
const float* out_result = NULL;
safe_xgboost(
XGBoosterPredictFromDMatrix(booster, dmat, config, &out_shape, &out_dim, &out_result));
assert(out_dim == 1);
assert(out_shape[0] == 1);
printf("%1.4f \n", out_result[0]);
safe_xgboost(XGDMatrixFree(dmat));
}
// free everything
safe_xgboost(XGBoosterFree(booster));
safe_xgboost(XGDMatrixFree(dtrain));
safe_xgboost(XGDMatrixFree(dtest));
return 0;
}
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