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Commit 044f79aa authored by Guolin Ke's avatar Guolin Ke
Browse files

support int64_t for CSR/CSR's indptr

parent 31b19afc
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Showing with 1667 additions and 280 deletions
include/LightGBM/c_api.h
View file @ 044f79aa
......@@ -26,6 +26,11 @@
typedef void* DatesetHandle;
typedef void* BoosterHandle;
#define dtype_float32 (0)
#define dtype_float64 (1)
#define dtype_int32 (2)
#define dtype_int64 (3)
/*!
* \brief get string message of the last error
* all function in this file will return 0 when success
......@@ -62,9 +67,10 @@ DllExport int LGBM_CreateDatasetFromBinaryFile(const char* filename,
/*!
* \brief create a dataset from CSR format
* \param indptr pointer to row headers
* \param indptr_type 0:int_32 1:int_64
* \param indices findex
* \param data fvalue
* \param float_type 0 for float_32 1 for float_64
* \param data_type 0 for float_32 1 for float_64
* \param nindptr number of rows in the matix + 1
* \param nelem number of nonzero elements in the matrix
* \param num_col number of columns; when it's set to 0, then guess from data
......@@ -73,13 +79,14 @@ DllExport int LGBM_CreateDatasetFromBinaryFile(const char* filename,
* \param out created dataset
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_CreateDatasetFromCSR(const int32_t* indptr,
DllExport int LGBM_CreateDatasetFromCSR(const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int float_type,
uint64_t nindptr,
uint64_t nelem,
uint64_t num_col,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
const char* parameters,
const DatesetHandle* reference,
DatesetHandle* out);
......@@ -87,9 +94,10 @@ DllExport int LGBM_CreateDatasetFromCSR(const int32_t* indptr,
/*!
* \brief create a dataset from CSC format
* \param col_ptr pointer to col headers
* \param col_ptr_type 0:int_32 1:int_64
* \param indices findex
* \param data fvalue
* \param float_type 0 for float_32 1 for float_64
* \param data_type 0 for float_32 1 for float_64
* \param ncol_ptr number of rows in the matix + 1
* \param nelem number of nonzero elements in the matrix
* \param num_row number of rows; when it's set to 0, then guess from data
......@@ -98,13 +106,14 @@ DllExport int LGBM_CreateDatasetFromCSR(const int32_t* indptr,
* \param out created dataset
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_CreateDatasetFromCSC(const int32_t* col_ptr,
DllExport int LGBM_CreateDatasetFromCSC(const void* col_ptr,
int col_ptr_type,
const int32_t* indices,
const void* data,
int float_type,
uint64_t ncol_ptr,
uint64_t nelem,
uint64_t num_row,
int data_type,
int64_t ncol_ptr,
int64_t nelem,
int64_t num_row,
const char* parameters,
const DatesetHandle* reference,
DatesetHandle* out);
......@@ -112,7 +121,7 @@ DllExport int LGBM_CreateDatasetFromCSC(const int32_t* col_ptr,
/*!
* \brief create dataset from dense matrix
* \param data pointer to the data space
* \param float_type 0 for float_32 1 for float_64
* \param data_type 0 for float_32 1 for float_64
* \param nrow number of rows
* \param ncol number columns
* \param is_row_major 1 for row major, 0 for column major
......@@ -122,7 +131,7 @@ DllExport int LGBM_CreateDatasetFromCSC(const int32_t* col_ptr,
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_CreateDatasetFromMat(const void* data,
int float_type,
int data_type,
int32_t nrow,
int32_t ncol,
int is_row_major,
......@@ -151,13 +160,13 @@ DllExport int LGBM_DatasetSaveBinary(DatesetHandle handle,
* \param field_name field name, can be label, weight, group
* \param field_data pointer to vector
* \param num_element number of element in field_data
* \param type float_32:0, uint32_t:1
* \param type float_32:0, int32_t:1
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_DatasetSetField(DatesetHandle handle,
const char* field_name,
const void* field_data,
uint64_t num_element,
int64_t num_element,
int type);
/*!
......@@ -166,12 +175,12 @@ DllExport int LGBM_DatasetSetField(DatesetHandle handle,
* \param field_name field name
* \param out_len used to set result length
* \param out_ptr pointer to the result
* \param out_type float_32:0, uint32_t:1
* \param out_type float_32:0, int32_t:1
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_DatasetGetField(DatesetHandle handle,
const char* field_name,
uint64_t* out_len,
int64_t* out_len,
const void** out_ptr,
int* out_type);
......@@ -182,7 +191,7 @@ DllExport int LGBM_DatasetGetField(DatesetHandle handle,
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_DatasetGetNumData(DatesetHandle handle,
uint64_t* out);
int64_t* out);
/*!
* \brief get number of features
......@@ -191,7 +200,7 @@ DllExport int LGBM_DatasetGetNumData(DatesetHandle handle,
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_DatasetGetNumFeature(DatesetHandle handle,
uint64_t* out);
int64_t* out);
// --- start Booster interfaces
......@@ -261,7 +270,7 @@ DllExport int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle,
*/
DllExport int LGBM_BoosterEval(BoosterHandle handle,
int data,
uint64_t* out_len,
int64_t* out_len,
float* out_results);
/*!
......@@ -272,7 +281,7 @@ DllExport int LGBM_BoosterEval(BoosterHandle handle,
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_BoosterGetScore(BoosterHandle handle,
uint64_t* out_len,
int64_t* out_len,
const float** out_result);
/*!
......@@ -286,16 +295,17 @@ this can be used to support customized eval function
*/
DllExport int LGBM_BoosterGetPredict(BoosterHandle handle,
int data,
uint64_t* out_len,
int64_t* out_len,
float* out_result);
/*!
* \brief make prediction for an new data set
* \param handle handle
* \param indptr pointer to row headers
* \param indptr_type 0:int_32 1:int_64
* \param indices findex
* \param data fvalue
* \param float_type 0:float_32 1:float64
* \param data_type 0:float_32 1:float64
* \param nindptr number of rows in the matix + 1
* \param nelem number of nonzero elements in the matrix
* \param num_col number of columns; when it's set to 0, then guess from data
......@@ -308,22 +318,23 @@ DllExport int LGBM_BoosterGetPredict(BoosterHandle handle,
* \return 0 when success, -1 when failure happens
*/
DllExport int LGBM_BoosterPredictForCSR(BoosterHandle handle,
const int32_t* indptr,
const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int float_type,
uint64_t nindptr,
uint64_t nelem,
uint64_t num_col,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
int predict_type,
uint64_t n_used_trees,
int64_t n_used_trees,
double* out_result);
/*!
* \brief make prediction for an new data set
* \param handle handle
* \param data pointer to the data space
* \param float_type 0:float_32 1:float64
* \param data_type 0:float_32 1:float64
* \param nrow number of rows
* \param ncol number columns
* \param is_row_major 1 for row major, 0 for column major
......@@ -337,12 +348,12 @@ DllExport int LGBM_BoosterPredictForCSR(BoosterHandle handle,
*/
DllExport int LGBM_BoosterPredictForMat(BoosterHandle handle,
const void* data,
int float_type,
int data_type,
int32_t nrow,
int32_t ncol,
int is_row_major,
int predict_type,
uint64_t n_used_trees,
int64_t n_used_trees,
double* out_result);
/*!
......@@ -356,4 +367,23 @@ DllExport int LGBM_BoosterSaveModel(BoosterHandle handle,
int num_used_model,
const char* filename);
std::function<std::vector<double>(int row_idx)>
RowFunctionFromDenseMatric(const void* data, int num_row, int num_col, int data_type, int is_row_major);
std::function<std::vector<std::pair<int, double>>(int row_idx)>
RowPairFunctionFromDenseMatric(const void* data, int num_row, int num_col, int data_type, int is_row_major);
std::function<std::vector<std::pair<int, double>>(int idx)>
RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices,
const void* data, int data_type, int64_t nindptr, int64_t nelem);
std::function<std::vector<std::pair<int, double>>(int idx)>
ColumnFunctionFromCSC(const void* col_ptr, int col_ptr_type, const int32_t* indices,
const void* data, int data_type, int64_t ncol_ptr, int64_t nelem);
std::vector<double>
SampleFromOneColumn(const std::vector<std::pair<int, double>>& data, const std::vector<size_t>& indices);
#endif // LIGHTGBM_C_API_H_
include/LightGBM/dataset.h
View file @ 044f79aa
......@@ -277,9 +277,13 @@ public:
void FinishLoad();
void SetField(const char* field_name, const void* field_data, data_size_t num_element, int type);
bool SetFloatField(const char* field_name, const float* field_data, data_size_t num_element);
void GetField(const char* field_name, uint64_t* out_len, const void** out_ptr, int* out_type);
bool SetIntField(const char* field_name, const int* field_data, data_size_t num_element);
bool GetFloatField(const char* field_name, int64_t* out_len, const float** out_ptr);
bool GetIntField(const char* field_name, int64_t* out_len, const int** out_ptr);
/*!
* \brief Save current dataset into binary file, will save to "filename.bin"
......
include/LightGBM/utils/common.h
View file @ 044f79aa
......@@ -393,181 +393,6 @@ inline void SortForPair(std::vector<T1>& keys, std::vector<T2>& values, size_t s
}
inline std::function<std::vector<double>(int row_idx)>
RowFunctionFromDenseMatric(const void* data, int num_row, int num_col, int float_type, int is_row_major) {
if (float_type == 0) {
const float* data_ptr = reinterpret_cast<const float*>(data);
if (is_row_major) {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<double> ret;
auto tmp_ptr = data_ptr + num_col * row_idx;
for (int i = 0; i < num_col; ++i) {
ret.push_back(static_cast<double>(*(tmp_ptr + i)));
}
return ret;
};
} else {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<double> ret;
for (int i = 0; i < num_col; ++i) {
ret.push_back(static_cast<double>(*(data_ptr + num_row * i + row_idx)));
}
return ret;
};
}
} else {
const double* data_ptr = reinterpret_cast<const double*>(data);
if (is_row_major) {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<double> ret;
auto tmp_ptr = data_ptr + num_col * row_idx;
for (int i = 0; i < num_col; ++i) {
ret.push_back(static_cast<double>(*(tmp_ptr + i)));
}
return ret;
};
} else {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<double> ret;
for (int i = 0; i < num_col; ++i) {
ret.push_back(static_cast<double>(*(data_ptr + num_row * i + row_idx)));
}
return ret;
};
}
}
}
inline std::function<std::vector<std::pair<int, double>>(int row_idx)>
RowPairFunctionFromDenseMatric(const void* data, int num_row, int num_col, int float_type, int is_row_major) {
if (float_type == 0) {
const float* data_ptr = reinterpret_cast<const float*>(data);
if (is_row_major) {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<std::pair<int, double>> ret;
auto tmp_ptr = data_ptr + num_col * row_idx;
for (int i = 0; i < num_col; ++i) {
ret.emplace_back(i, static_cast<double>(*(tmp_ptr + i)));
}
return ret;
};
} else {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<std::pair<int, double>> ret;
for (int i = 0; i < num_col; ++i) {
ret.emplace_back(i, static_cast<double>(*(data_ptr + num_row * i + row_idx)));
}
return ret;
};
}
} else {
const double* data_ptr = reinterpret_cast<const double*>(data);
if (is_row_major) {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<std::pair<int, double>> ret;
auto tmp_ptr = data_ptr + num_col * row_idx;
for (int i = 0; i < num_col; ++i) {
ret.emplace_back(i, static_cast<double>(*(tmp_ptr + i)));
}
return ret;
};
} else {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<std::pair<int, double>> ret;
for (int i = 0; i < num_col; ++i) {
ret.emplace_back(i, static_cast<double>(*(data_ptr + num_row * i + row_idx)));
}
return ret;
};
}
}
}
inline std::function<std::vector<std::pair<int, double>>(int idx)>
RowFunctionFromCSR(const int32_t* indptr, const int32_t* indices, const void* data, int float_type, uint64_t nindptr, uint64_t nelem) {
if (float_type == 0) {
const float* data_ptr = reinterpret_cast<const float*>(data);
return [indptr, indices, data_ptr, nindptr, nelem](int idx) {
CHECK(idx + 1 < nindptr);
std::vector<std::pair<int, double>> ret;
int32_t start = indptr[idx];
int32_t end = indptr[idx + 1];
CHECK(start >= 0 && end < nelem);
for (int32_t i = start; i <= end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else {
const double* data_ptr = reinterpret_cast<const double*>(data);
return [indptr, indices, data_ptr, nindptr, nelem](int idx) {
CHECK(idx + 1 < nindptr);
std::vector<std::pair<int, double>> ret;
int32_t start = indptr[idx];
int32_t end = indptr[idx + 1];
CHECK(start >= 0 && end <= nelem);
for (int32_t i = start; i < end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
}
}
inline std::function<std::vector<std::pair<int, double>>(int idx)>
ColumnFunctionFromCSC(const int32_t* col_ptr, const int32_t* indices, const void* data, int float_type, uint64_t ncol_ptr, uint64_t nelem) {
if (float_type == 0) {
const float* data_ptr = reinterpret_cast<const float*>(data);
return [col_ptr, indices, data_ptr, ncol_ptr, nelem](int idx) {
CHECK(idx + 1 < ncol_ptr);
std::vector<std::pair<int, double>> ret;
int32_t start = col_ptr[idx];
int32_t end = col_ptr[idx + 1];
CHECK(start >= 0 && end <= nelem);
for (int32_t i = start; i < end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else {
const double* data_ptr = reinterpret_cast<const double*>(data);
return [col_ptr, indices, data_ptr, ncol_ptr, nelem](int idx) {
CHECK(idx + 1 < ncol_ptr);
std::vector<std::pair<int, double>> ret;
int32_t start = col_ptr[idx];
int32_t end = col_ptr[idx + 1];
CHECK(start >= 0 && end <= nelem);
for (int32_t i = start; i < end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
}
}
inline std::vector<double> SampleFromOneColumn(const std::vector<std::pair<int, double>>& data, const std::vector<size_t>& indices) {
size_t j = 0;
std::vector<double> ret;
for (auto row_idx : indices) {
while (j < data.size() && data[j].first < row_idx) {
++j;
}
if (j < data.size() && data[j].first == row_idx) {
ret.push_back(data[j].second);
} else {
ret.push_back(0);
}
}
return ret;
}
} // namespace Common
......
model.txt 0 → 100644
View file @ 044f79aa
gbdt
num_class=1
label_index=0
max_feature_idx=27
sigmoid=1
Tree=0
num_leaves=31
split_feature=25 26 27 25 25 25 5 26 22 24 27 27 24 27 5 22 25 25 22 3 22 21 24 9 25 27 27 24 26 8
split_gain=323.274 172.669 161.444 63.5242 59.7581 58.4657 42.3384 41.1274 38.7844 83.353 34.2467 33.4865 34.8204 27.7654 25.0331 18.443 16.296 15.2477 14.8161 14.2556 14.1083 13.9722 13.806 13.395 13.3459 12.7317 12.6624 11.062 10.9597 10.6446
threshold=1.258 0.7935 0.8895 1.0155 0.6975 0.6255 0.8165 0.8705 1.0255 1.0515 0.8015 0.8895 1.2295 0.8015 0.8755 1.0685 0.9485 0.5315 0.9635 1.2575 1.0255 1.0105 1.1285 0.9925 0.8525 1.1155 1.0405 0.7385 1.3435 1.63
left_child=1 6 3 4 17 15 27 10 9 26 24 18 20 23 -5 22 -11 -3 -2 -14 -13 25 -4 -8 -6 -10 -7 -1 -22 -18
right_child=11 2 5 14 7 8 13 -9 21 16 -12 12 19 -15 -16 -17 29 -19 -20 -21 28 -23 -24 -25 -26 -27 -28 -29 -30 -31
leaf_parent=27 18 17 22 14 24 26 23 7 25 16 10 20 19 13 14 15 29 17 18 19 28 21 22 23 24 25 26 27 28 29
leaf_value=-0.0140845 -0.0282609 0.000552486 -0.0697479 -0.0182796 0.0706161 -0.0222798 0.0104 0.077221 0.0613208 0.0396104 -0.0106796 -0.0721519 -0.007173 -0.0333333 0.0375887 -0 -0.02 0.0373737 0.0144828 -0.0454545 -0.02 0.0202429 -0.0210526 0.0508197 0.0296875 0.0192661 -0.0617021 -0.0359841 -0.0618321 0.0217949
Tree=1
num_leaves=31
split_feature=25 26 27 5 22 24 25 5 25 27 24 26 27 27 25 25 25 25 22 22 3 25 5 21 5 9 27 21 24 11
split_gain=262.499 140.244 131.487 59.0112 47.4766 77.5193 41.0359 34.3244 28.2251 27.2895 28.496 24.0347 22.5148 21.0124 20.5652 12.9557 12.6074 19.1012 12.0103 11.6114 11.5913 11.2654 11.3893 11.1306 12.1953 10.9032 9.90073 9.3901 8.99114 8.98656
threshold=1.258 0.7935 0.8895 0.8755 1.0685 1.0515 1.0155 0.8165 0.6255 0.8895 1.2295 0.8705 0.8015 0.7765 0.6255 0.6975 0.6975 0.9485 0.9635 1.0255 1.2575 1.0155 0.8165 1.0105 1.1595 0.9925 1.1155 0.9075 0.7385 0.5755
left_child=1 7 3 6 5 26 8 28 -3 18 19 13 25 -10 -7 -6 -5 -18 -2 -11 -12 22 -16 -17 -25 -9 27 -4 29 -1
right_child=9 2 4 16 15 14 -8 12 11 10 20 -13 -14 -15 21 23 17 -19 -20 -21 -22 -23 -24 24 -26 -27 -28 -29 -30 -31
leaf_parent=29 18 8 27 16 15 14 6 25 13 19 20 11 12 13 22 23 17 17 18 19 20 21 22 24 24 25 26 27 28 29
leaf_value=-0.00299902 -0.0254559 -0.000635171 -0.0433215 0.0379715 0.00251514 -0.021192 -0.0164573 0.00936105 0.0433754 -0.0652879 -0.00645604 0.058106 -0.0300346 -0.0079561 0.0100664 0.0482247 0.07678 0.0428371 0.0130373 -0.037736 -0.0409968 0.00207257 0.0441918 0.0077831 0.0432166 0.0458603 -0.0621846 -0.0101954 -0.0324292 -0.0344763
Tree=2
num_leaves=31
split_feature=25 25 25 26 27 26 5 27 24 0 13 27 24 3 22 26 27 9 5 25 27 5 25 22 26 5 22 22 3 26
split_gain=213.981 118.36 77.4382 70.49 61.0345 39.504 35.9959 31.5067 29.3239 26.7482 25.4754 22.449 23.6855 17.7805 17.4327 16.851 24.5734 16.1392 14.3749 13.4762 13.2462 12.7853 14.4944 11.9538 11.8497 10.1947 9.85056 9.7484 9.48474 9.05357
threshold=1.258 0.6255 0.9485 0.7935 0.8895 0.8705 0.8165 0.9295 1.1285 1.624 0.9435 0.8895 1.2295 1.6055 0.9895 0.8705 0.8015 1.1685 0.8755 0.7585 0.8895 0.8165 0.8075 0.7695 0.7385 0.7555 1.0255 0.9635 1.2575 1.3435
left_child=1 10 3 6 15 25 24 18 14 13 17 27 26 21 -9 16 -5 23 -7 -17 -12 22 -6 -1 -3 -4 -13 -2 -14 -28
right_child=11 2 5 4 9 7 -8 8 -10 -11 20 12 28 -15 -16 19 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 29 -29 -30 -31
leaf_parent=23 27 24 25 16 22 18 6 14 8 9 20 26 28 13 14 19 16 17 18 19 20 21 22 23 24 25 29 27 28 29
leaf_value=-0.00224187 -0.0229605 -0.0318271 -0.0486103 0.0518834 -0.00862919 0.0123235 0.0362477 -0.0562241 0.0152451 -0.0178404 0.0220013 -0.0596057 -0.00581125 6.1226e-005 -0.003962 0.0386211 -0.00180855 0.0141107 0.0469138 0.0699541 -0.0131327 0.0461554 0.0416686 -0.0315822 0.00496198 -0.0114242 -0.0142803 0.0117403 -0.0371068 -0.0524243
Tree=3
num_leaves=31
split_feature=25 25 5 27 26 27 25 26 24 13 25 0 24 9 26 3 22 27 25 25 3 5 25 25 11 21 12 5 27 3
split_gain=176.304 115.046 69.2943 53.6743 52.0111 34.3232 33.5997 21.9406 21.0834 20.6789 18.118 16.1715 14.6949 14.0245 12.957 11.0566 11.4083 10.7377 10.5604 10.4174 10.3208 11.1713 10.0118 9.71273 9.32358 9.03531 8.84163 8.16498 9.14738 7.2913
threshold=1.1095 0.6255 0.8165 0.9295 0.7935 0.8895 1.4645 0.9005 0.9885 0.9435 0.9005 1.624 0.7385 0.9925 0.8705 1.6055 1.0685 0.8895 0.9485 0.7585 1.6055 1.4405 0.8525 0.8525 -0.5815 0.8835 1.661 0.9985 1.0405 1.4035
left_child=1 9 4 14 -3 7 8 10 12 13 -6 20 -2 26 22 16 -8 25 23 -16 21 -5 -4 -7 -14 -11 29 -10 -29 -1
right_child=6 2 3 11 5 18 15 -9 27 17 -12 -13 24 -15 19 -17 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 -28 28 -30 -31
leaf_parent=29 12 4 22 21 10 23 16 7 27 25 10 11 24 13 19 15 16 17 18 19 20 21 22 23 24 25 26 28 28 29
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right_child=1 2 8 7 -6 -7 -8 -9 -10 10 11 -13 18 14 15 -17 -18 -19 19 -21 -22 28 -24 25 27 26 -28 -29 -30 -31
leaf_parent=17 1 6 8 7 4 5 6 7 8 10 11 11 29 14 15 15 16 17 20 19 20 28 22 25 27 26 26 27 28 29
leaf_value=-0.00190519 -0.0163111 0.0129395 0.0214539 -0.00150737 -0.015427 0.0226322 -0.0151498 -0.0189064 -2.98734e-005 -0.0127144 0.0292796 -0.00456562 0.0128369 -0.0173743 0.0249088 -0.0171046 -0.00809835 0.0246778 0.00899959 -0.00197434 0.0356549 -0.0179241 -0.00543061 -0.0136446 -0.0117552 0.0248182 -0.00934478 0.0172252 0.00570636 0.0359091
Tree=95
num_leaves=31
split_feature=13 14 7 24 16 10 27 7 6 13 27 13 1 6 24 0 2 7 10 9 1 10 18 1 0 7 3 24 14 2
split_gain=0.553146 5.23594 5.21069 2.41379 2.54047 2.10643 2.02316 1.05381 3.26806 3.73006 4.50261 5.17599 5.43299 1.42381 3.14824 3.31782 1.97851 2.99784 3.84132 2.40872 3.58384 2.25594 4.24371 7.31538 4.43531 3.86056 3.6626 4.24336 3.25871 4.58227
threshold=1.494 0.1045 -0.8325 0.9405 1.911 0.0745 0.9775 -1.2805 -0.8425 1.1115 1.0405 0.8715 -0.1105 -1.445 0.8225 0.9025 1.2875 -0.3245 -0.1055 0.7915 -0.1105 0.6235 0.0915 -0.2845 1.2245 -0.5835 1.0285 0.8975 -0.2675 -0.579
left_child=7 2 -2 -4 -5 -3 -7 8 -1 10 11 12 -10 14 -9 -16 21 18 -18 -19 -21 25 23 -23 28 26 27 -15 -24 -30
right_child=1 5 3 4 -6 6 -8 13 9 -11 -12 -13 -14 16 15 -17 17 19 -20 20 -22 22 24 -25 -26 -27 -28 -29 29 -31
leaf_parent=8 2 5 3 4 4 6 6 14 12 9 10 11 12 27 15 15 18 19 18 20 20 23 28 23 24 25 26 27 29 29
leaf_value=0.0186005 -0.0279911 0.0220683 -0.0115497 -0.00342544 0.021757 0.0163274 -0.00573278 0.00646441 -0.0107071 0.0158124 -0.0246335 -0.0196044 0.0241732 -0.0131264 -0.0316321 -0.00264123 0.0197793 -0.0214589 -0.00943816 -0.0198468 0.00942146 0.018758 0.0231758 -0.0128455 0.0276202 0.00107988 -0.0199604 0.00977868 -0.0181098 0.00978018
Tree=96
num_leaves=31
split_feature=0 5 6 6 27 7 21 16 13 10 4 5 13 10 6 18 18 14 9 18 19 15 22 14 5 5 23 18 23 23
split_gain=0.658036 5.8098 4.20081 4.63825 4.30031 3.93139 4.14189 4.13315 3.76824 2.8736 2.13856 1.80641 6.86851 3.06862 2.1881 3.29248 1.57424 3.99249 4.0543 3.41276 5.26451 5.77221 2.91298 4.17581 2.55326 5.47764 5.41109 2.46905 5.57183 3.57733
threshold=1.3985 0.6385 -1.0705 1.102 1.1155 0.3735 0.7995 0.637 0.9435 0.0745 -0.6025 0.4825 1.0255 0.0745 -1.445 -0.4555 -0.6495 -1.4895 1.4495 -0.2715 1.0475 -0.5565 0.8555 0.1045 0.8165 0.9365 0.9915 -0.0915 0.9875 0.9985
left_child=11 9 -3 4 5 6 -4 -8 -6 -2 -7 12 13 -1 15 -13 17 -16 24 20 21 -18 -23 -24 -19 -26 -27 28 -21 -29
right_child=1 2 3 -5 8 10 7 -9 -10 -11 -12 14 -14 -15 16 -17 19 18 -20 27 -22 22 23 -25 25 26 -28 29 -30 -31
leaf_parent=13 9 2 6 3 8 10 7 7 8 9 10 15 12 13 17 15 21 24 18 28 20 22 23 23 25 26 26 29 28 29
leaf_value=-0.00973007 0.0271089 0.0120746 -0.0326129 0.00906122 -0.0456416 0.0182638 0.00661133 -0.024525 -0.0134916 0.00135997 -0.00497608 0.00631238 -0.0315507 0.013615 0.019685 -0.0179786 0.0355199 -0.00787796 -0.0223741 0.00999168 -0.0110808 -0.00930512 0.00178308 0.0321877 0.0219492 -0.014055 0.0129162 -0.000102714 -0.0214887 0.00964431
Tree=97
num_leaves=31
split_feature=0 5 21 7 23 10 14 10 19 22 7 6 5 13 24 6 9 22 1 2 17 3 21 19 26 7 9 24 16 19
split_gain=0.536167 4.7151 3.72451 3.46141 5.52128 2.44116 2.38177 2.32939 2.04312 2.70275 2.61235 2.00395 1.46534 5.57594 2.74827 1.77202 3.22644 1.56172 6.24755 2.60662 2.318 2.69941 1.98183 1.83414 3.96496 4.39231 3.3559 2.87498 2.60098 3.09555
threshold=1.3985 0.6385 0.8835 0.3735 0.9915 0.8375 0.4535 0.0745 -0.8125 0.9375 -0.3245 -0.2615 0.4825 1.0255 0.8605 -1.445 1.0735 0.7695 0.8945 1.048 0.8375 0.9335 0.5755 1.2475 1.1975 0.1395 0.8545 0.6175 0.637 -0.8125
left_child=12 7 3 6 -5 8 11 -2 -4 -10 -11 -3 13 14 -1 16 -14 18 19 20 21 -17 -19 27 25 26 -25 -24 29 -29
right_child=1 2 5 4 -6 -7 -8 -9 9 10 -12 -13 15 -15 -16 17 -18 22 -20 -21 -22 -23 23 24 -26 -27 -28 28 -30 -31
leaf_parent=14 7 11 8 4 4 5 6 7 9 10 10 11 16 13 14 21 16 22 18 19 20 21 27 26 24 25 26 29 28 29
leaf_value=0.0120187 0.0244103 -0.0151268 0.00896234 -0.020669 0.0197701 0.0154616 -0.00656068 0.00122409 0.00776801 -0.00084551 -0.0251975 -0.038897 -0.0176859 -0.0284523 -0.0101759 -0.00380802 0.00609678 0.0117492 -0.0162757 -0.00355794 0.0298347 0.0220691 0.0138832 -0.0139129 0.0140274 -0.0267716 0.0129113 -0.00967615 0.0041183 0.000675487
Tree=98
num_leaves=31
split_feature=25 2 11 0 18 0 5 5 16 11 2 10 16 24 9 27 26 25 14 20 25 14 22 25 21 19 17 14 10 14
split_gain=0.516183 2.05023 2.12822 2.49174 5.8295 4.80052 2.55856 0.990636 2.60687 3.10542 4.47154 2.67616 1.11052 3.42773 3.38543 3.2377 5.95796 4.85156 3.09515 3.05881 3.00402 2.94332 2.77581 2.95073 3.94709 3.24359 2.49711 3.54136 2.68293 2.41826
threshold=1.4645 1.048 1.2975 0.6805 -0.6495 0.9905 1.1595 1.4405 1.911 -0.8145 -0.1025 -0.2965 0.637 1.3685 1.0735 0.8275 0.7385 0.5315 -0.0735 0.7755 1.258 0.6495 0.9115 0.8075 0.8565 -0.3615 0.5785 0.1045 -0.1055 -0.0735
left_child=7 2 3 6 -5 -6 -2 12 9 -9 -11 -10 13 -1 18 16 -14 -17 -15 21 22 26 29 -24 -25 -26 -18 28 -28 -19
right_child=1 -3 -4 4 5 -7 -8 8 11 10 -12 -13 15 14 -16 17 19 20 -20 -21 -22 -23 23 24 25 -27 27 -29 -30 -31
leaf_parent=13 6 1 2 4 5 5 6 9 11 10 10 11 16 18 14 17 26 29 18 19 20 21 23 24 25 25 28 27 28 29
leaf_value=-0.000106127 -0.00111397 0.00950508 -0.0225095 -0.0202757 0.0289603 -0.00283725 -0.0255918 -0.018863 -9.88802e-005 -0.0124548 0.0198458 0.0261335 -0.00867 0.0164283 0.0271903 -0.0275088 0.014574 -0.0246636 -0.00919733 0.025473 -0.0192388 0.0221286 -0.00863461 0.0269484 0.015488 -0.00745179 -0.00734307 -0.0227933 0.0168415 -0.00162723
Tree=99
num_leaves=31
split_feature=25 24 13 18 21 11 7 2 3 22 26 17 13 13 2 19 21 2 9 16 0 18 16 9 26 6 2 18 1 23
split_gain=0.468182 1.96477 2.77786 4.60921 3.82145 4.73596 3.84675 3.51879 3.65402 3.80815 3.40133 3.34042 3.23498 3.23396 2.30097 2.10779 4.05897 2.60487 3.16783 2.58462 1.7768 1.28092 3.13933 5.68839 4.15015 3.99878 3.20392 2.48463 3.29205 3.81067
threshold=0.9485 1.2295 0.6065 0.2795 1.1015 -0.5815 1.2855 1.2875 0.5005 0.9635 1.0565 1.0875 1.2095 0.7995 -0.1025 -1.2825 1.3005 0.8325 1.2935 0.637 0.7495 0.8775 1.911 0.6075 0.9645 0.4465 -0.3345 -0.0915 0.8945 0.9945
left_child=21 2 3 -2 5 13 7 8 -7 12 -6 -12 -10 -4 -15 -3 17 18 19 -17 -11 27 23 -23 25 -25 -24 -1 29 -29
right_child=1 15 4 -5 10 6 -8 -9 9 20 11 -13 -14 14 -16 16 -18 -19 -20 -21 -22 22 26 24 -26 -27 -28 28 -30 -31
leaf_parent=27 3 15 13 3 10 8 6 7 12 20 11 11 12 14 14 19 16 17 18 19 20 23 26 25 24 25 26 29 28 29
leaf_value=-0.000915941 -0.026126 0.0177388 0.00302545 0.00430722 0.0220376 -0.0211765 0.0198918 0.0177302 0.0190108 -0.0230601 0.0139579 -0.015012 -0.00880847 -0.00941417 -0.0300373 0.0129009 -0.0152787 -0.00756247 0.0254242 -0.00936412 -0.0012408 -0.030852 -0.00977537 0.0253692 -0.0169773 -0.00718978 0.0145032 0.00370448 -0.00446472 0.0185589
feature importances:
Column_25=202
Column_27=179
Column_22=169
Column_0=162
Column_24=158
Column_5=151
Column_26=150
Column_9=145
Column_21=127
Column_3=124
Column_13=116
Column_23=112
Column_11=109
Column_6=100
Column_10=97
Column_1=95
Column_18=94
Column_19=91
Column_7=88
Column_17=88
Column_14=87
Column_2=79
Column_4=69
Column_15=59
Column_8=51
Column_16=42
Column_12=30
Column_20=26
src/c_api.cpp
View file @ 044f79aa
......@@ -183,7 +183,7 @@ DllExport int LGBM_CreateDatasetFromBinaryFile(const char* filename,
}
DllExport int LGBM_CreateDatasetFromMat(const void* data,
int float_type,
int data_type,
int32_t nrow,
int32_t ncol,
int is_row_major,
......@@ -195,7 +195,7 @@ DllExport int LGBM_CreateDatasetFromMat(const void* data,
config.LoadFromString(parameters);
DatasetLoader loader(config.io_config, nullptr);
Dataset* ret = nullptr;
auto get_row_fun = Common::RowFunctionFromDenseMatric(data, nrow, ncol, float_type, is_row_major);
auto get_row_fun = RowFunctionFromDenseMatric(data, nrow, ncol, data_type, is_row_major);
if (reference == nullptr) {
// sample data first
Random rand(config.io_config.data_random_seed);
......@@ -226,13 +226,14 @@ DllExport int LGBM_CreateDatasetFromMat(const void* data,
return 0;
}
DllExport int LGBM_CreateDatasetFromCSR(const int32_t* indptr,
DllExport int LGBM_CreateDatasetFromCSR(const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int float_type,
uint64_t nindptr,
uint64_t nelem,
uint64_t num_col,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
const char* parameters,
const DatesetHandle* reference,
DatesetHandle* out) {
......@@ -241,7 +242,7 @@ DllExport int LGBM_CreateDatasetFromCSR(const int32_t* indptr,
config.LoadFromString(parameters);
DatasetLoader loader(config.io_config, nullptr);
Dataset* ret = nullptr;
auto get_row_fun = Common::RowFunctionFromCSR(indptr, indices, data, float_type, nindptr, nelem);
auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem);
int32_t nrow = static_cast<int32_t>(nindptr - 1);
if (reference == nullptr) {
// sample data first
......@@ -269,7 +270,7 @@ DllExport int LGBM_CreateDatasetFromCSR(const int32_t* indptr,
sample_values[inner_data.first][i] = inner_data.second;
}
}
CHECK(num_col >= sample_values.size());
CHECK(num_col >= static_cast<int>(sample_values.size()));
ret = loader.CostructFromSampleData(sample_values, nrow);
} else {
ret = new Dataset(nrow, config.io_config.num_class);
......@@ -289,13 +290,14 @@ DllExport int LGBM_CreateDatasetFromCSR(const int32_t* indptr,
}
DllExport int LGBM_CreateDatasetFromCSC(const int32_t* col_ptr,
DllExport int LGBM_CreateDatasetFromCSC(const void* col_ptr,
int col_ptr_type,
const int32_t* indices,
const void* data,
int float_type,
uint64_t ncol_ptr,
uint64_t nelem,
uint64_t num_row,
int data_type,
int64_t ncol_ptr,
int64_t nelem,
int64_t num_row,
const char* parameters,
const DatesetHandle* reference,
DatesetHandle* out) {
......@@ -303,7 +305,7 @@ DllExport int LGBM_CreateDatasetFromCSC(const int32_t* col_ptr,
config.LoadFromString(parameters);
DatasetLoader loader(config.io_config, nullptr);
Dataset* ret = nullptr;
auto get_col_fun = Common::ColumnFunctionFromCSC(col_ptr, indices, data, float_type, ncol_ptr, nelem);
auto get_col_fun = ColumnFunctionFromCSC(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem);
int32_t nrow = static_cast<int32_t>(num_row);
if (reference == nullptr) {
Log::Warning("Construct from CSC format is not efficient");
......@@ -315,7 +317,7 @@ DllExport int LGBM_CreateDatasetFromCSC(const int32_t* col_ptr,
#pragma omp parallel for schedule(guided)
for (int i = 0; i < static_cast<int>(sample_values.size()); ++i) {
auto cur_col = get_col_fun(i);
sample_values[i] = Common::SampleFromOneColumn(cur_col, sample_indices);
sample_values[i] = SampleFromOneColumn(cur_col, sample_indices);
}
ret = loader.CostructFromSampleData(sample_values, nrow);
} else {
......@@ -350,34 +352,44 @@ DllExport int LGBM_DatasetSaveBinary(DatesetHandle handle,
DllExport int LGBM_DatasetSetField(DatesetHandle handle,
const char* field_name,
const void* field_data,
uint64_t num_element,
int64_t num_element,
int type) {
auto dataset = reinterpret_cast<Dataset*>(handle);
dataset->SetField(field_name, field_data, static_cast<int32_t>(num_element), type);
return 0;
bool is_success = false;
if (type == dtype_float32) {
is_success = dataset->SetFloatField(field_name, reinterpret_cast<const float*>(field_data), static_cast<int32_t>(num_element));
} else if (type == dtype_int32) {
is_success = dataset->SetIntField(field_name, reinterpret_cast<const int*>(field_data), static_cast<int32_t>(num_element));
}
if (is_success) { return 0; }
return -1;
}
DllExport int LGBM_DatasetGetField(DatesetHandle handle,
const char* field_name,
uint64_t* out_len,
int64_t* out_len,
const void** out_ptr,
int* out_type) {
auto dataset = reinterpret_cast<Dataset*>(handle);
dataset->GetField(field_name, out_len, out_ptr, out_type);
return 0;
if (dataset->GetFloatField(field_name, out_len, reinterpret_cast<const float**>(out_ptr))) {
*out_type = dtype_float32;
return 0;
} else if (dataset->GetIntField(field_name, out_len, reinterpret_cast<const int**>(out_ptr))) {
*out_type = dtype_int32;
return 0;
}
return -1;
}
DllExport int LGBM_DatasetGetNumData(DatesetHandle handle,
uint64_t* out) {
int64_t* out) {
auto dataset = reinterpret_cast<Dataset*>(handle);
*out = dataset->num_data();
return 0;
}
DllExport int LGBM_DatasetGetNumFeature(DatesetHandle handle,
uint64_t* out) {
int64_t* out) {
auto dataset = reinterpret_cast<Dataset*>(handle);
*out = dataset->num_total_features();
return 0;
......@@ -442,13 +454,13 @@ DllExport int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle,
DllExport int LGBM_BoosterEval(BoosterHandle handle,
int data,
uint64_t* out_len,
int64_t* out_len,
float* out_results) {
Booster* ref_booster = reinterpret_cast<Booster*>(handle);
auto boosting = ref_booster->GetBoosting();
auto result_buf = boosting->GetEvalAt(data);
*out_len = static_cast<uint64_t>(result_buf.size());
*out_len = static_cast<int64_t>(result_buf.size());
for (size_t i = 0; i < result_buf.size(); ++i) {
(out_results)[i] = static_cast<float>(result_buf[i]);
}
......@@ -456,47 +468,48 @@ DllExport int LGBM_BoosterEval(BoosterHandle handle,
}
DllExport int LGBM_BoosterGetScore(BoosterHandle handle,
uint64_t* out_len,
int64_t* out_len,
const float** out_result) {
Booster* ref_booster = reinterpret_cast<Booster*>(handle);
auto boosting = ref_booster->GetBoosting();
int len = 0;
*out_result = boosting->GetTrainingScore(&len);
*out_len = static_cast<uint64_t>(len);
*out_len = static_cast<int64_t>(len);
return 0;
}
DllExport int LGBM_BoosterGetPredict(BoosterHandle handle,
int data,
uint64_t* out_len,
int64_t* out_len,
float* out_result) {
Booster* ref_booster = reinterpret_cast<Booster*>(handle);
auto boosting = ref_booster->GetBoosting();
int len = 0;
boosting->GetPredictAt(data, out_result, &len);
*out_len = static_cast<uint64_t>(len);
*out_len = static_cast<int64_t>(len);
return 0;
}
DllExport int LGBM_BoosterPredictForCSR(BoosterHandle handle,
const int32_t* indptr,
const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int float_type,
uint64_t nindptr,
uint64_t nelem,
uint64_t,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t,
int predict_type,
uint64_t n_used_trees,
int64_t n_used_trees,
double* out_result) {
Booster* ref_booster = reinterpret_cast<Booster*>(handle);
ref_booster->PrepareForPrediction(static_cast<int>(n_used_trees), predict_type);
auto get_row_fun = Common::RowFunctionFromCSR(indptr, indices, data, float_type, nindptr, nelem);
auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem);
int num_class = ref_booster->NumberOfClasses();
int nrow = static_cast<int>(nindptr - 1);
#pragma omp parallel for schedule(guided)
......@@ -512,18 +525,18 @@ DllExport int LGBM_BoosterPredictForCSR(BoosterHandle handle,
DllExport int LGBM_BoosterPredictForMat(BoosterHandle handle,
const void* data,
int float_type,
int data_type,
int32_t nrow,
int32_t ncol,
int is_row_major,
int predict_type,
uint64_t n_used_trees,
int64_t n_used_trees,
double* out_result) {
Booster* ref_booster = reinterpret_cast<Booster*>(handle);
ref_booster->PrepareForPrediction(static_cast<int>(n_used_trees), predict_type);
auto get_row_fun = Common::RowPairFunctionFromDenseMatric(data, nrow, ncol, float_type, is_row_major);
auto get_row_fun = RowPairFunctionFromDenseMatric(data, nrow, ncol, data_type, is_row_major);
int num_class = ref_booster->NumberOfClasses();
#pragma omp parallel for schedule(guided)
for (int i = 0; i < nrow; ++i) {
......@@ -544,3 +557,261 @@ DllExport int LGBM_BoosterSaveModel(BoosterHandle handle,
ref_booster->SaveModelToFile(num_used_model, filename);
return 0;
}
std::function<std::vector<double>(int row_idx)>
RowFunctionFromDenseMatric(const void* data, int num_row, int num_col, int data_type, int is_row_major) {
if (data_type == dtype_float32) {
const float* data_ptr = reinterpret_cast<const float*>(data);
if (is_row_major) {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<double> ret;
auto tmp_ptr = data_ptr + num_col * row_idx;
for (int i = 0; i < num_col; ++i) {
ret.push_back(static_cast<double>(*(tmp_ptr + i)));
}
return ret;
};
} else {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<double> ret;
for (int i = 0; i < num_col; ++i) {
ret.push_back(static_cast<double>(*(data_ptr + num_row * i + row_idx)));
}
return ret;
};
}
} else if (data_type == dtype_float64) {
const double* data_ptr = reinterpret_cast<const double*>(data);
if (is_row_major) {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<double> ret;
auto tmp_ptr = data_ptr + num_col * row_idx;
for (int i = 0; i < num_col; ++i) {
ret.push_back(static_cast<double>(*(tmp_ptr + i)));
}
return ret;
};
} else {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<double> ret;
for (int i = 0; i < num_col; ++i) {
ret.push_back(static_cast<double>(*(data_ptr + num_row * i + row_idx)));
}
return ret;
};
}
} else {
Log::Fatal("unknown data type in RowFunctionFromDenseMatric");
}
}
std::function<std::vector<std::pair<int, double>>(int row_idx)>
RowPairFunctionFromDenseMatric(const void* data, int num_row, int num_col, int data_type, int is_row_major) {
if (data_type == dtype_float32) {
const float* data_ptr = reinterpret_cast<const float*>(data);
if (is_row_major) {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<std::pair<int, double>> ret;
auto tmp_ptr = data_ptr + num_col * row_idx;
for (int i = 0; i < num_col; ++i) {
ret.emplace_back(i, static_cast<double>(*(tmp_ptr + i)));
}
return ret;
};
} else {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<std::pair<int, double>> ret;
for (int i = 0; i < num_col; ++i) {
ret.emplace_back(i, static_cast<double>(*(data_ptr + num_row * i + row_idx)));
}
return ret;
};
}
} else if (data_type == dtype_float64) {
const double* data_ptr = reinterpret_cast<const double*>(data);
if (is_row_major) {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<std::pair<int, double>> ret;
auto tmp_ptr = data_ptr + num_col * row_idx;
for (int i = 0; i < num_col; ++i) {
ret.emplace_back(i, static_cast<double>(*(tmp_ptr + i)));
}
return ret;
};
} else {
return [data_ptr, num_col, num_row](int row_idx) {
CHECK(row_idx < num_row);
std::vector<std::pair<int, double>> ret;
for (int i = 0; i < num_col; ++i) {
ret.emplace_back(i, static_cast<double>(*(data_ptr + num_row * i + row_idx)));
}
return ret;
};
}
} else {
Log::Fatal("unknown data type in RowPairFunctionFromDenseMatric");
}
}
std::function<std::vector<std::pair<int, double>>(int idx)>
RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t nindptr, int64_t nelem) {
if (data_type == dtype_float32) {
const float* data_ptr = reinterpret_cast<const float*>(data);
if (indptr_type == dtype_int32) {
const int32_t* ptr_indptr = reinterpret_cast<const int32_t*>(indptr);
return [ptr_indptr, indices, data_ptr, nindptr, nelem](int idx) {
CHECK(idx + 1 < nindptr);
std::vector<std::pair<int, double>> ret;
int64_t start = ptr_indptr[idx];
int64_t end = ptr_indptr[idx + 1];
CHECK(start >= 0 && end < nelem);
for (int64_t i = start; i <= end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else if (indptr_type == dtype_int64) {
const int64_t* ptr_indptr = reinterpret_cast<const int64_t*>(indptr);
return [ptr_indptr, indices, data_ptr, nindptr, nelem](int idx) {
CHECK(idx + 1 < nindptr);
std::vector<std::pair<int, double>> ret;
int64_t start = ptr_indptr[idx];
int64_t end = ptr_indptr[idx + 1];
CHECK(start >= 0 && end < nelem);
for (int64_t i = start; i <= end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else {
Log::Fatal("unknown data type in RowFunctionFromCSR");
}
} else if (data_type == dtype_float64) {
const double* data_ptr = reinterpret_cast<const double*>(data);
if (indptr_type == dtype_int32) {
const int32_t* ptr_indptr = reinterpret_cast<const int32_t*>(indptr);
return [ptr_indptr, indices, data_ptr, nindptr, nelem](int idx) {
CHECK(idx + 1 < nindptr);
std::vector<std::pair<int, double>> ret;
int64_t start = ptr_indptr[idx];
int64_t end = ptr_indptr[idx + 1];
CHECK(start >= 0 && end < nelem);
for (int64_t i = start; i <= end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else if (indptr_type == dtype_int64) {
const int64_t* ptr_indptr = reinterpret_cast<const int64_t*>(indptr);
return [ptr_indptr, indices, data_ptr, nindptr, nelem](int idx) {
CHECK(idx + 1 < nindptr);
std::vector<std::pair<int, double>> ret;
int64_t start = ptr_indptr[idx];
int64_t end = ptr_indptr[idx + 1];
CHECK(start >= 0 && end < nelem);
for (int64_t i = start; i <= end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else {
Log::Fatal("unknown data type in RowFunctionFromCSR");
}
} else {
Log::Fatal("unknown data type in RowFunctionFromCSR");
}
}
std::function<std::vector<std::pair<int, double>>(int idx)>
ColumnFunctionFromCSC(const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t nelem) {
if (data_type == dtype_float32) {
const float* data_ptr = reinterpret_cast<const float*>(data);
if (col_ptr_type == dtype_int32) {
const int32_t* ptr_col_ptr = reinterpret_cast<const int32_t*>(col_ptr);
return [ptr_col_ptr, indices, data_ptr, ncol_ptr, nelem](int idx) {
CHECK(idx + 1 < ncol_ptr);
std::vector<std::pair<int, double>> ret;
int64_t start = ptr_col_ptr[idx];
int64_t end = ptr_col_ptr[idx + 1];
CHECK(start >= 0 && end <= nelem);
for (int64_t i = start; i < end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else if (col_ptr_type == dtype_int64) {
const int64_t* ptr_col_ptr = reinterpret_cast<const int64_t*>(col_ptr);
return [ptr_col_ptr, indices, data_ptr, ncol_ptr, nelem](int idx) {
CHECK(idx + 1 < ncol_ptr);
std::vector<std::pair<int, double>> ret;
int64_t start = ptr_col_ptr[idx];
int64_t end = ptr_col_ptr[idx + 1];
CHECK(start >= 0 && end <= nelem);
for (int64_t i = start; i < end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else {
Log::Fatal("unknown data type in ColumnFunctionFromCSC");
}
} else if (data_type == dtype_float64) {
const double* data_ptr = reinterpret_cast<const double*>(data);
if (col_ptr_type == dtype_int32) {
const int32_t* ptr_col_ptr = reinterpret_cast<const int32_t*>(col_ptr);
return [ptr_col_ptr, indices, data_ptr, ncol_ptr, nelem](int idx) {
CHECK(idx + 1 < ncol_ptr);
std::vector<std::pair<int, double>> ret;
int64_t start = ptr_col_ptr[idx];
int64_t end = ptr_col_ptr[idx + 1];
CHECK(start >= 0 && end <= nelem);
for (int64_t i = start; i < end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else if (col_ptr_type == dtype_int64) {
const int64_t* ptr_col_ptr = reinterpret_cast<const int64_t*>(col_ptr);
return [ptr_col_ptr, indices, data_ptr, ncol_ptr, nelem](int idx) {
CHECK(idx + 1 < ncol_ptr);
std::vector<std::pair<int, double>> ret;
int64_t start = ptr_col_ptr[idx];
int64_t end = ptr_col_ptr[idx + 1];
CHECK(start >= 0 && end <= nelem);
for (int64_t i = start; i < end; ++i) {
ret.emplace_back(indices[i], data_ptr[i]);
}
return ret;
};
} else {
Log::Fatal("unknown data type in ColumnFunctionFromCSC");
}
} else {
Log::Fatal("unknown data type in ColumnFunctionFromCSC");
}
}
std::vector<double> SampleFromOneColumn(const std::vector<std::pair<int, double>>& data, const std::vector<size_t>& indices) {
size_t j = 0;
std::vector<double> ret;
for (auto row_idx : indices) {
while (j < data.size() && data[j].first < static_cast<int>(row_idx)) {
++j;
}
if (j < data.size() && data[j].first == static_cast<int>(row_idx)) {
ret.push_back(data[j].second);
} else {
ret.push_back(0);
}
}
return ret;
}
\ No newline at end of file
src/io/dataset.cpp
View file @ 044f79aa
......@@ -64,57 +64,60 @@ std::vector<const BinMapper*> Dataset::GetBinMappers() const {
return ret;
}
void Dataset::SetField(const char* field_name, const void* field_data, data_size_t num_element, int type) {
bool Dataset::SetFloatField(const char* field_name, const float* field_data, data_size_t num_element) {
std::string name(field_name);
name = Common::Trim(name);
if (name == std::string("label") || name == std::string("target")) {
if (type != 0) {
Log::Fatal("type of label should be float");
}
metadata_.SetLabel(reinterpret_cast<const float*>(field_data), num_element);
metadata_.SetLabel(field_data, num_element);
} else if (name == std::string("weight") || name == std::string("weights")) {
if (type != 0) {
Log::Fatal("type of weights should be float");
}
metadata_.SetWeights(reinterpret_cast<const float*>(field_data), num_element);
metadata_.SetWeights(field_data, num_element);
} else if (name == std::string("init_score")) {
if (type != 0) {
Log::Fatal("type of init_score should be float");
}
metadata_.SetInitScore(reinterpret_cast<const float*>(field_data), num_element);
} else if (name == std::string("query") || name == std::string("group")) {
if (type != 1) {
Log::Fatal("type of init_score should be int");
}
metadata_.SetQueryBoundaries(reinterpret_cast<const data_size_t*>(field_data), num_element);
metadata_.SetInitScore(field_data, num_element);
} else {
Log::Fatal("unknow field name: %s", field_name);
return false;
}
return true;
}
void Dataset::GetField(const char* field_name, uint64_t* out_len, const void** out_ptr, int* out_type) {
bool Dataset::SetIntField(const char* field_name, const int* field_data, data_size_t num_element) {
std::string name(field_name);
name = Common::Trim(name);
if (name == std::string("query") || name == std::string("group")) {
metadata_.SetQueryBoundaries(field_data, num_element);
} else {
return false;
}
return true;
}
bool Dataset::GetFloatField(const char* field_name, int64_t* out_len, const float** out_ptr) {
std::string name(field_name);
name = Common::Trim(name);
if (name == std::string("label") || name == std::string("target")) {
*out_ptr = metadata_.label();
*out_len = num_data_;
*out_type = 0;
} else if (name == std::string("weight") || name == std::string("weights")) {
*out_ptr = metadata_.weights();
*out_len = num_data_;
*out_type = 0;
} else if (name == std::string("init_score")) {
*out_ptr = metadata_.init_score();
*out_len = num_data_;
*out_type = 0;
} else if (name == std::string("query") || name == std::string("group")) {
} else {
return false;
}
return true;
}
bool Dataset::GetIntField(const char* field_name, int64_t* out_len, const int** out_ptr) {
std::string name(field_name);
name = Common::Trim(name);
if (name == std::string("query") || name == std::string("group")) {
*out_ptr = metadata_.query_boundaries();
*out_len = num_data_;
*out_type = 1;
} else {
Log::Fatal("unknow field name: %s", field_name);
return false;
}
return true;
}
void Dataset::SaveBinaryFile(const char* bin_filename) {
......
src/io/dataset_loader.cpp
View file @ 044f79aa
......@@ -511,7 +511,10 @@ std::vector<std::string> DatasetLoader::LoadTextDataToMemory(const char* filenam
}
std::vector<std::string> DatasetLoader::SampleTextDataFromMemory(const std::vector<std::string>& data) {
const size_t sample_cnt = static_cast<size_t>(data.size() < io_config_.bin_construct_sample_cnt ? data.size() : io_config_.bin_construct_sample_cnt);
size_t sample_cnt = static_cast<size_t>(io_config_.bin_construct_sample_cnt);
if (sample_cnt > data.size()) {
sample_cnt = data.size();
}
std::vector<size_t> sample_indices = random_.Sample(data.size(), sample_cnt);
std::vector<std::string> out;
for (size_t i = 0; i < sample_indices.size(); ++i) {
......
test_dll.py 0 → 100644
View file @ 044f79aa
from __future__ import absolute_import
import sys
import os
import ctypes
import collections
import re
import numpy as np
from scipy import sparse
def _load_lib():
"""Load xgboost Library."""
lib_path = './windows/x64/DLL/lib_lightgbm.dll'
if len(lib_path) == 0:
return None
lib = ctypes.cdll.LoadLibrary(lib_path)
return lib
LIB = _load_lib()
LIB.LGBM_GetLastError.restype = ctypes.c_char_p
def test_load_from_file():
handle = ctypes.c_void_p()
LIB.LGBM_CreateDatasetFromFile(ctypes.c_char_p('./examples/binary_classification/binary.train'),
ctypes.c_char_p('max_bin=15'), ctypes.c_void_p(None), ctypes.byref(handle))
num_data = ctypes.c_ulong()
LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data) )
print num_data
num_feature = ctypes.c_ulong()
LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature) )
return handle
def c_array(ctype, values):
"""Convert a python string to c array."""
return (ctype * len(values))(*values)
def c_str(string):
"""Convert a python string to cstring."""
return ctypes.c_char_p(string.encode('utf-8'))
def test_load_from_matric():
data = []
inp = open('./examples/binary_classification/binary.train', 'r')
for line in inp.readlines():
data.append( [float(x) for x in line.split('\t')[1:]] )
inp.close()
mat = np.array(data)
print mat.shape
data = np.array(mat.reshape(mat.size), copy=False)
handle = ctypes.c_void_p()
LIB.LGBM_CreateDatasetFromMat(data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), 1,
mat.shape[0],
mat.shape[1], 1,
ctypes.c_char_p('max_bin=15 is_sparse=false'), None, ctypes.byref(handle) )
LIB.LGBM_DatasetFree(ctypes.byref(handle))
# num_data = ctypes.c_ulong()
# LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data) )
# print num_data
# num_feature = ctypes.c_ulong()
# LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature) )
# print num_feature
return handle
def test_load_from_csr(filename, reference):
data = []
label = []
inp = open(filename, 'r')
for line in inp.readlines():
data.append( [float(x) for x in line.split('\t')[1:]] )
label.append( float(line.split('\t')[0]) )
inp.close()
mat = np.array(data)
label = np.array(label, dtype=np.float32)
print mat.shape
csr = sparse.csr_matrix(mat)
handle = ctypes.c_void_p()
ref = None
if reference != None:
ref = ctypes.byref(reference)
LIB.LGBM_CreateDatasetFromCSR(c_array(ctypes.c_int, csr.indptr),
c_array(ctypes.c_int, csr.indices),
csr.data.ctypes.data_as(ctypes.POINTER(ctypes.c_void_p)),
1, len(csr.indptr), len(csr.data),
csr.shape[1], ctypes.c_char_p('max_bin=15'), ref, ctypes.byref(handle) )
num_data = ctypes.c_ulong()
LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data) )
print num_data
num_feature = ctypes.c_ulong()
LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature) )
LIB.LGBM_DatasetSetField(handle, c_str('label'), c_array(ctypes.c_float, label), len(label), 0)
return handle
train = test_load_from_csr('./examples/binary_classification/binary.train', None)
test = [test_load_from_csr('./examples/binary_classification/binary.test', train)]
name = [c_str('test')]
booster = ctypes.c_void_p()
LIB.LGBM_BoosterCreate(train, c_array(ctypes.c_void_p, test), c_array(ctypes.c_char_p, name), 1, "app=binary metric=auc num_leaves=31", ctypes.byref(booster))
is_finished = ctypes.c_int(0)
for i in xrange(100):
LIB.LGBM_BoosterUpdateOneIter(booster,ctypes.byref(is_finished))
result = np.array([0.0], dtype=np.float32)
out_len = ctypes.c_ulong(0)
LIB.LGBM_BoosterEval(booster, 0, ctypes.byref(out_len), result.ctypes.data_as(ctypes.POINTER(ctypes.c_float)))
print result
LIB.LGBM_BoosterSaveModel(booster, -1, c_str('model.txt'))
booster2 = ctypes.c_void_p()
LIB.LGBM_BoosterLoadFromModelfile(c_str('model.txt'), ctypes.byref(booster2))
print type(len([0,0]))
\ No newline at end of file
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