[Breaking] Accept multi-dim meta info. (#7405)
This PR changes base_margin into a 3-dim array, with one of them being reserved for multi-target classification. Also, a breaking change is made for binary serialization due to extra dimension along with a fix for saving the feature weights. Lastly, it unifies the prediction initialization between CPU and GPU. After this PR, the meta info setter in Python will be based on array interface.
This commit is contained in:
Jiaming Yuan
GitHub
parent
9fb4338964
commit
d33854af1b
@ -134,3 +134,49 @@ Similarly, if you want to exclude C++ source from linting:
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cd /path/to/xgboost/
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python3 tests/ci_build/tidy.py --cpp=0
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**********************************
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Guide for handling user input data
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**********************************
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This is an in-comprehensive guide for handling user input data. XGBoost has wide verity
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of native supported data structures, mostly come from higher level language bindings. The
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inputs ranges from basic contiguous 1 dimension memory buffer to more sophisticated data
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structures like columnar data with validity mask. Raw input data can be used in 2 places,
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firstly it's the construction of various ``DMatrix``, secondly it's the in-place
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prediction. For plain memory buffer, there's not much to discuss since it's just a
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pointer with a size. But for general n-dimension array and columnar data, there are many
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subtleties. XGBoost has 3 different data structures for handling optionally masked arrays
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(tensors), for consuming user inputs ``ArrayInterface`` should be chosen. There are many
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existing functions that accept only plain pointer due to legacy reasons (XGBoost started
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as a much simpler library and didn't care about memory usage that much back then). The
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``ArrayInterface`` is a in memory representation of ``__array_interface__`` protocol
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defined by numpy or the ``__cuda_array_interface__`` defined by numba. Following is a
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check list of things to have in mind when accepting related user inputs:
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- [ ] Is it strided? (identified by the ``strides`` field)
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- [ ] If it's a vector, is it row vector or column vector? (Identified by both ``shape``
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and ``strides``).
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- [ ] Is the data type supported? Half type and 128 integer types should be converted
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before going into XGBoost.
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- [ ] Does it have higher than 1 dimension? (identified by ``shape`` field)
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- [ ] Are some of dimensions trivial? (shape[dim] <= 1)
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- [ ] Does it have mask? (identified by ``mask`` field)
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- [ ] Can the mask be broadcasted? (unsupported at the moment)
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- [ ] Is it on CUDA memory? (identified by ``data`` field, and optionally ``stream``)
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Most of the checks are handled by the ``ArrayInterface`` during construction, except for
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the data type issue since it doesn't know how to cast such pointers with C builtin types.
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But for safety reason one should still try to write related tests for the all items. The
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data type issue should be taken care of in language binding for each of the specific data
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input. For single-chunk columnar format, it's just a masked array for each column so it
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should be treated uniformly as normal array. For input predictor ``X``, we have adapters
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for each type of input. Some are composition of the others. For instance, CSR matrix has 3
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potentially strided arrays for ``indptr``, ``indices`` and ``values``. No assumption
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should be made to these components (all the check boxes should be considered). Slicing row
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of CSR matrix should calculate the offset of each field based on respective strides.
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For meta info like labels, which is growing both in size and complexity, we accept only
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masked array at the moment (no specialized adapter). One should be careful about the
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input data shape. For base margin it can be 2 dim or higher if we have multiple targets in
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the future. The getters in ``DMatrix`` returns only 1 dimension flatten vectors at the
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moment, which can be improved in the future when it's needed.
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@ -249,7 +249,7 @@ XGB_DLL int XGDMatrixCreateFromCudaArrayInterface(char const *data,
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char const* json_config,
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DMatrixHandle *out);
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/*
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/**
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* ========================== Begin data callback APIs =========================
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*
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* Short notes for data callback
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@ -258,9 +258,9 @@ XGB_DLL int XGDMatrixCreateFromCudaArrayInterface(char const *data,
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* used by JVM packages. It uses `XGBoostBatchCSR` to accept batches for CSR formated
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* input, and concatenate them into 1 final big CSR. The related functions are:
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*
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* - XGBCallbackSetData
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* - XGBCallbackDataIterNext
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* - XGDMatrixCreateFromDataIter
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* - \ref XGBCallbackSetData
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* - \ref XGBCallbackDataIterNext
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* - \ref XGDMatrixCreateFromDataIter
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*
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* Another set is used by external data iterator. It accept foreign data iterators as
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* callbacks. There are 2 different senarios where users might want to pass in callbacks
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@ -276,17 +276,17 @@ XGB_DLL int XGDMatrixCreateFromCudaArrayInterface(char const *data,
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* Related functions are:
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*
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* # Factory functions
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* - `XGDMatrixCreateFromCallback` for external memory
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* - `XGDeviceQuantileDMatrixCreateFromCallback` for quantile DMatrix
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* - \ref XGDMatrixCreateFromCallback for external memory
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* - \ref XGDeviceQuantileDMatrixCreateFromCallback for quantile DMatrix
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*
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* # Proxy that callers can use to pass data to XGBoost
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* - XGProxyDMatrixCreate
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* - XGDMatrixCallbackNext
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* - DataIterResetCallback
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* - XGProxyDMatrixSetDataCudaArrayInterface
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* - XGProxyDMatrixSetDataCudaColumnar
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* - XGProxyDMatrixSetDataDense
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* - XGProxyDMatrixSetDataCSR
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* - \ref XGProxyDMatrixCreate
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* - \ref XGDMatrixCallbackNext
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* - \ref DataIterResetCallback
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* - \ref XGProxyDMatrixSetDataCudaArrayInterface
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* - \ref XGProxyDMatrixSetDataCudaColumnar
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* - \ref XGProxyDMatrixSetDataDense
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* - \ref XGProxyDMatrixSetDataCSR
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* - ... (data setters)
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*/
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@ -411,7 +411,7 @@ XGB_EXTERN_C typedef void DataIterResetCallback(DataIterHandle handle); // NOLIN
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* - cache_prefix: The path of cache file, caller must initialize all the directories in this path.
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* - nthread (optional): Number of threads used for initializing DMatrix.
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*
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* \param out The created external memory DMatrix
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* \param[out] out The created external memory DMatrix
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*
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* \return 0 when success, -1 when failure happens
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*/
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@ -605,7 +605,8 @@ XGB_DLL int XGDMatrixSetUIntInfo(DMatrixHandle handle,
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* char const* feat_names [] {"feat_0", "feat_1"};
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* XGDMatrixSetStrFeatureInfo(handle, "feature_name", feat_names, 2);
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*
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* // i for integer, q for quantitive. Similarly "int" and "float" are also recognized.
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* // i for integer, q for quantitive, c for categorical. Similarly "int" and "float"
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* // are also recognized.
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* char const* feat_types [] {"i", "q"};
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* XGDMatrixSetStrFeatureInfo(handle, "feature_type", feat_types, 2);
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*
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@ -47,7 +47,7 @@ enum class FeatureType : uint8_t {
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class MetaInfo {
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public:
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/*! \brief number of data fields in MetaInfo */
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static constexpr uint64_t kNumField = 11;
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static constexpr uint64_t kNumField = 12;
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/*! \brief number of rows in the data */
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uint64_t num_row_{0}; // NOLINT
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@ -69,7 +69,7 @@ class MetaInfo {
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* if specified, xgboost will start from this init margin
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* can be used to specify initial prediction to boost from.
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*/
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HostDeviceVector<bst_float> base_margin_; // NOLINT
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linalg::Tensor<float, 3> base_margin_; // NOLINT
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/*!
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* \brief lower bound of the label, to be used for survival analysis (censored regression)
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*/
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@ -154,12 +154,8 @@ class MetaInfo {
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* \brief Set information in the meta info with array interface.
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* \param key The key of the information.
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* \param interface_str String representation of json format array interface.
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*
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* [ column_0, column_1, ... column_n ]
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*
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* Right now only 1 column is permitted.
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*/
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void SetInfo(StringView key, std::string const& interface_str);
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void SetInfo(StringView key, StringView interface_str);
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void GetInfo(char const* key, bst_ulong* out_len, DataType dtype,
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const void** out_dptr) const;
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@ -181,6 +177,9 @@ class MetaInfo {
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void Extend(MetaInfo const& that, bool accumulate_rows, bool check_column);
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private:
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void SetInfoFromHost(StringView key, Json arr);
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void SetInfoFromCUDA(StringView key, Json arr);
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/*! \brief argsort of labels */
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mutable std::vector<size_t> label_order_cache_;
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};
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@ -479,7 +478,7 @@ class DMatrix {
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this->Info().SetInfo(key, dptr, dtype, num);
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}
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virtual void SetInfo(const char* key, std::string const& interface_str) {
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this->Info().SetInfo(key, interface_str);
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this->Info().SetInfo(key, StringView{interface_str});
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}
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/*! \brief meta information of the dataset */
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virtual const MetaInfo& Info() const = 0;
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@ -19,7 +19,7 @@ namespace xgboost {
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*/
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class IntrusivePtrCell {
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private:
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std::atomic<int32_t> count_;
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std::atomic<int32_t> count_ {0};
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template <typename T> friend class IntrusivePtr;
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std::int32_t IncRef() noexcept {
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@ -31,7 +31,7 @@ class IntrusivePtrCell {
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bool IsZero() const { return Count() == 0; }
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public:
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IntrusivePtrCell() noexcept : count_{0} {}
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IntrusivePtrCell() noexcept = default;
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int32_t Count() const { return count_.load(std::memory_order_relaxed); }
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};
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@ -126,9 +126,8 @@ class Predictor {
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* \param out_predt Prediction vector to be initialized.
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* \param model Tree model used for prediction.
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*/
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virtual void InitOutPredictions(const MetaInfo &info,
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HostDeviceVector<bst_float> *out_predt,
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const gbm::GBTreeModel &model) const = 0;
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void InitOutPredictions(const MetaInfo& info, HostDeviceVector<bst_float>* out_predt,
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const gbm::GBTreeModel& model) const;
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/**
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* \brief Generate batch predictions for a given feature matrix. May use
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@ -33,7 +33,7 @@ struct ObjInfo {
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bool const_hess{false};
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explicit ObjInfo(Task t) : task{t} {}
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ObjInfo(Task t, bool khess) : const_hess{khess} {}
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ObjInfo(Task t, bool khess) : task{t}, const_hess{khess} {}
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};
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} // namespace xgboost
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#endif // XGBOOST_TASK_H_
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@ -5,7 +5,7 @@ import ctypes
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import json
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import warnings
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import os
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from typing import Any, Tuple, Callable, Optional, List
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from typing import Any, Tuple, Callable, Optional, List, Union
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import numpy as np
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@ -138,14 +138,14 @@ def _is_numpy_array(data):
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return isinstance(data, (np.ndarray, np.matrix))
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def _ensure_np_dtype(data, dtype):
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def _ensure_np_dtype(data, dtype) -> Tuple[np.ndarray, np.dtype]:
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if data.dtype.hasobject or data.dtype in [np.float16, np.bool_]:
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data = data.astype(np.float32, copy=False)
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dtype = np.float32
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return data, dtype
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def _maybe_np_slice(data, dtype):
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def _maybe_np_slice(data: np.ndarray, dtype) -> np.ndarray:
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'''Handle numpy slice. This can be removed if we use __array_interface__.
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'''
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try:
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@ -852,23 +852,17 @@ def _validate_meta_shape(data: Any, name: str) -> None:
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def _meta_from_numpy(
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data: np.ndarray, field: str, dtype, handle: ctypes.c_void_p
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data: np.ndarray,
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field: str,
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dtype: Optional[Union[np.dtype, str]],
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handle: ctypes.c_void_p,
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) -> None:
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data = _maybe_np_slice(data, dtype)
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data, dtype = _ensure_np_dtype(data, dtype)
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interface = data.__array_interface__
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assert interface.get('mask', None) is None, 'Masked array is not supported'
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size = data.size
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c_type = _to_data_type(str(data.dtype), field)
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ptr = interface['data'][0]
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ptr = ctypes.c_void_p(ptr)
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_check_call(_LIB.XGDMatrixSetDenseInfo(
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handle,
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c_str(field),
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ptr,
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c_bst_ulong(size),
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c_type
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))
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if interface.get("mask", None) is not None:
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raise ValueError("Masked array is not supported.")
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interface_str = _array_interface(data)
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_check_call(_LIB.XGDMatrixSetInfoFromInterface(handle, c_str(field), interface_str))
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def _meta_from_list(data, field, dtype, handle):
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@ -911,7 +905,9 @@ def _meta_from_dt(data, field: str, dtype, handle: ctypes.c_void_p):
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_meta_from_numpy(data, field, dtype, handle)
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def dispatch_meta_backend(matrix: DMatrix, data, name: str, dtype: str = None):
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def dispatch_meta_backend(
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matrix: DMatrix, data, name: str, dtype: Optional[Union[str, np.dtype]] = None
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):
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'''Dispatch for meta info.'''
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handle = matrix.handle
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assert handle is not None
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@ -12,7 +12,8 @@ int AllVisibleGPUs() {
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// When compiled with CUDA but running on CPU only device,
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// cudaGetDeviceCount will fail.
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dh::safe_cuda(cudaGetDeviceCount(&n_visgpus));
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} catch(const dmlc::Error &except) {
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} catch (const dmlc::Error &) {
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cudaGetLastError(); // reset error.
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return 0;
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}
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return n_visgpus;
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339
src/data/data.cc
339
src/data/data.cc
@ -3,6 +3,7 @@
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* \file data.cc
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*/
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#include <dmlc/registry.h>
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#include <array>
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#include <cstring>
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#include "dmlc/io.h"
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@ -12,10 +13,13 @@
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#include "xgboost/logging.h"
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#include "xgboost/version_config.h"
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#include "xgboost/learner.h"
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#include "xgboost/string_view.h"
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#include "sparse_page_writer.h"
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#include "simple_dmatrix.h"
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#include "../common/io.h"
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#include "../common/linalg_op.h"
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#include "../common/math.h"
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#include "../common/version.h"
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#include "../common/group_data.h"
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@ -66,10 +70,22 @@ void SaveVectorField(dmlc::Stream* strm, const std::string& name,
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SaveVectorField(strm, name, type, shape, field.ConstHostVector());
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}
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template <typename T, int32_t D>
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void SaveTensorField(dmlc::Stream* strm, const std::string& name, xgboost::DataType type,
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const xgboost::linalg::Tensor<T, D>& field) {
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strm->Write(name);
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strm->Write(static_cast<uint8_t>(type));
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strm->Write(false); // is_scalar=False
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for (size_t i = 0; i < D; ++i) {
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strm->Write(field.Shape(i));
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}
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strm->Write(field.Data()->HostVector());
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}
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template <typename T>
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void LoadScalarField(dmlc::Stream* strm, const std::string& expected_name,
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xgboost::DataType expected_type, T* field) {
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const std::string invalid {"MetaInfo: Invalid format. "};
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const std::string invalid{"MetaInfo: Invalid format for " + expected_name};
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std::string name;
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xgboost::DataType type;
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bool is_scalar;
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@ -91,7 +107,7 @@ void LoadScalarField(dmlc::Stream* strm, const std::string& expected_name,
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template <typename T>
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void LoadVectorField(dmlc::Stream* strm, const std::string& expected_name,
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xgboost::DataType expected_type, std::vector<T>* field) {
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const std::string invalid {"MetaInfo: Invalid format. "};
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const std::string invalid{"MetaInfo: Invalid format for " + expected_name};
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std::string name;
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xgboost::DataType type;
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bool is_scalar;
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@ -124,6 +140,33 @@ void LoadVectorField(dmlc::Stream* strm, const std::string& expected_name,
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LoadVectorField(strm, expected_name, expected_type, &field->HostVector());
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}
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template <typename T, int32_t D>
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void LoadTensorField(dmlc::Stream* strm, std::string const& expected_name,
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xgboost::DataType expected_type, xgboost::linalg::Tensor<T, D>* p_out) {
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const std::string invalid{"MetaInfo: Invalid format for " + expected_name};
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std::string name;
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xgboost::DataType type;
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bool is_scalar;
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CHECK(strm->Read(&name)) << invalid;
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CHECK_EQ(name, expected_name) << invalid << " Expected field: " << expected_name
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<< ", got: " << name;
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uint8_t type_val;
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CHECK(strm->Read(&type_val)) << invalid;
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type = static_cast<xgboost::DataType>(type_val);
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CHECK(type == expected_type) << invalid
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<< "Expected field of type: " << static_cast<int>(expected_type)
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<< ", "
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<< "got field type: " << static_cast<int>(type);
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CHECK(strm->Read(&is_scalar)) << invalid;
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CHECK(!is_scalar) << invalid << "Expected field " << expected_name
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<< " to be a tensor; got a scalar";
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std::array<size_t, D> shape;
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for (size_t i = 0; i < D; ++i) {
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CHECK(strm->Read(&(shape[i])));
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}
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auto& field = p_out->Data()->HostVector();
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CHECK(strm->Read(&field)) << invalid;
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}
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} // anonymous namespace
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namespace xgboost {
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@ -136,25 +179,26 @@ void MetaInfo::Clear() {
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labels_.HostVector().clear();
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group_ptr_.clear();
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weights_.HostVector().clear();
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base_margin_.HostVector().clear();
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base_margin_ = decltype(base_margin_){};
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}
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/*
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* Binary serialization format for MetaInfo:
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*
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* | name | type | is_scalar | num_row | num_col | value |
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* |--------------------+----------+-----------+---------+---------+-------------------------|
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* | num_row | kUInt64 | True | NA | NA | ${num_row_} |
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* | num_col | kUInt64 | True | NA | NA | ${num_col_} |
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* | num_nonzero | kUInt64 | True | NA | NA | ${num_nonzero_} |
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* | labels | kFloat32 | False | ${size} | 1 | ${labels_} |
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* | group_ptr | kUInt32 | False | ${size} | 1 | ${group_ptr_} |
|
||||
* | weights | kFloat32 | False | ${size} | 1 | ${weights_} |
|
||||
* | base_margin | kFloat32 | False | ${size} | 1 | ${base_margin_} |
|
||||
* | labels_lower_bound | kFloat32 | False | ${size} | 1 | ${labels_lower_bound_} |
|
||||
* | labels_upper_bound | kFloat32 | False | ${size} | 1 | ${labels_upper_bound_} |
|
||||
* | feature_names | kStr | False | ${size} | 1 | ${feature_names} |
|
||||
* | feature_types | kStr | False | ${size} | 1 | ${feature_types} |
|
||||
* | name | type | is_scalar | num_row | num_col | dim3 | value |
|
||||
* |--------------------+----------+-----------+-------------+-------------+-------------+------------------------|
|
||||
* | num_row | kUInt64 | True | NA | NA | NA | ${num_row_} |
|
||||
* | num_col | kUInt64 | True | NA | NA | NA | ${num_col_} |
|
||||
* | num_nonzero | kUInt64 | True | NA | NA | NA | ${num_nonzero_} |
|
||||
* | labels | kFloat32 | False | ${size} | 1 | NA | ${labels_} |
|
||||
* | group_ptr | kUInt32 | False | ${size} | 1 | NA | ${group_ptr_} |
|
||||
* | weights | kFloat32 | False | ${size} | 1 | NA | ${weights_} |
|
||||
* | base_margin | kFloat32 | False | ${Shape(0)} | ${Shape(1)} | ${Shape(2)} | ${base_margin_} |
|
||||
* | labels_lower_bound | kFloat32 | False | ${size} | 1 | NA | ${labels_lower_bound_} |
|
||||
* | labels_upper_bound | kFloat32 | False | ${size} | 1 | NA | ${labels_upper_bound_} |
|
||||
* | feature_names | kStr | False | ${size} | 1 | NA | ${feature_names} |
|
||||
* | feature_types | kStr | False | ${size} | 1 | NA | ${feature_types} |
|
||||
* | feature_types | kFloat32 | False | ${size} | 1 | NA | ${feature_weights} |
|
||||
*
|
||||
* Note that the scalar fields (is_scalar=True) will have num_row and num_col missing.
|
||||
* Also notice the difference between the saved name and the name used in `SetInfo':
|
||||
@ -175,8 +219,7 @@ void MetaInfo::SaveBinary(dmlc::Stream *fo) const {
|
||||
{group_ptr_.size(), 1}, group_ptr_); ++field_cnt;
|
||||
SaveVectorField(fo, u8"weights", DataType::kFloat32,
|
||||
{weights_.Size(), 1}, weights_); ++field_cnt;
|
||||
SaveVectorField(fo, u8"base_margin", DataType::kFloat32,
|
||||
{base_margin_.Size(), 1}, base_margin_); ++field_cnt;
|
||||
SaveTensorField(fo, u8"base_margin", DataType::kFloat32, base_margin_); ++field_cnt;
|
||||
SaveVectorField(fo, u8"labels_lower_bound", DataType::kFloat32,
|
||||
{labels_lower_bound_.Size(), 1}, labels_lower_bound_); ++field_cnt;
|
||||
SaveVectorField(fo, u8"labels_upper_bound", DataType::kFloat32,
|
||||
@ -186,6 +229,9 @@ void MetaInfo::SaveBinary(dmlc::Stream *fo) const {
|
||||
{feature_names.size(), 1}, feature_names); ++field_cnt;
|
||||
SaveVectorField(fo, u8"feature_types", DataType::kStr,
|
||||
{feature_type_names.size(), 1}, feature_type_names); ++field_cnt;
|
||||
SaveVectorField(fo, u8"feature_weights", DataType::kFloat32, {feature_weights.Size(), 1},
|
||||
feature_weights);
|
||||
++field_cnt;
|
||||
|
||||
CHECK_EQ(field_cnt, kNumField) << "Wrong number of fields";
|
||||
}
|
||||
@ -214,10 +260,14 @@ void MetaInfo::LoadBinary(dmlc::Stream *fi) {
|
||||
auto major = std::get<0>(version);
|
||||
// MetaInfo is saved in `SparsePageSource'. So the version in MetaInfo represents the
|
||||
// version of DMatrix.
|
||||
CHECK_EQ(major, 1) << "Binary DMatrix generated by XGBoost: "
|
||||
<< Version::String(version) << " is no longer supported. "
|
||||
std::stringstream msg;
|
||||
msg << "Binary DMatrix generated by XGBoost: " << Version::String(version)
|
||||
<< " is no longer supported. "
|
||||
<< "Please process and save your data in current version: "
|
||||
<< Version::String(Version::Self()) << " again.";
|
||||
CHECK_EQ(major, 1) << msg.str();
|
||||
auto minor = std::get<1>(version);
|
||||
CHECK_GE(minor, 6) << msg.str();
|
||||
|
||||
const uint64_t expected_num_field = kNumField;
|
||||
uint64_t num_field { 0 };
|
||||
@ -244,12 +294,13 @@ void MetaInfo::LoadBinary(dmlc::Stream *fi) {
|
||||
LoadVectorField(fi, u8"labels", DataType::kFloat32, &labels_);
|
||||
LoadVectorField(fi, u8"group_ptr", DataType::kUInt32, &group_ptr_);
|
||||
LoadVectorField(fi, u8"weights", DataType::kFloat32, &weights_);
|
||||
LoadVectorField(fi, u8"base_margin", DataType::kFloat32, &base_margin_);
|
||||
LoadTensorField(fi, u8"base_margin", DataType::kFloat32, &base_margin_);
|
||||
LoadVectorField(fi, u8"labels_lower_bound", DataType::kFloat32, &labels_lower_bound_);
|
||||
LoadVectorField(fi, u8"labels_upper_bound", DataType::kFloat32, &labels_upper_bound_);
|
||||
|
||||
LoadVectorField(fi, u8"feature_names", DataType::kStr, &feature_names);
|
||||
LoadVectorField(fi, u8"feature_types", DataType::kStr, &feature_type_names);
|
||||
LoadVectorField(fi, u8"feature_weights", DataType::kFloat32, &feature_weights);
|
||||
LoadFeatureType(feature_type_names, &feature_types.HostVector());
|
||||
}
|
||||
|
||||
@ -292,10 +343,13 @@ MetaInfo MetaInfo::Slice(common::Span<int32_t const> ridxs) const {
|
||||
if (this->base_margin_.Size() != this->num_row_) {
|
||||
CHECK_EQ(this->base_margin_.Size() % this->num_row_, 0)
|
||||
<< "Incorrect size of base margin vector.";
|
||||
size_t stride = this->base_margin_.Size() / this->num_row_;
|
||||
out.base_margin_.HostVector() = Gather(this->base_margin_.HostVector(), ridxs, stride);
|
||||
auto margin = this->base_margin_.View(this->base_margin_.Data()->DeviceIdx());
|
||||
out.base_margin_.Reshape(ridxs.size(), margin.Shape()[1], margin.Shape()[2]);
|
||||
size_t stride = margin.Stride(0);
|
||||
out.base_margin_.Data()->HostVector() =
|
||||
Gather(this->base_margin_.Data()->HostVector(), ridxs, stride);
|
||||
} else {
|
||||
out.base_margin_.HostVector() = Gather(this->base_margin_.HostVector(), ridxs);
|
||||
out.base_margin_.Data()->HostVector() = Gather(this->base_margin_.Data()->HostVector(), ridxs);
|
||||
}
|
||||
|
||||
out.feature_weights.Resize(this->feature_weights.Size());
|
||||
@ -338,62 +392,91 @@ inline bool MetaTryLoadFloatInfo(const std::string& fname,
|
||||
return true;
|
||||
}
|
||||
|
||||
// macro to dispatch according to specified pointer types
|
||||
#define DISPATCH_CONST_PTR(dtype, old_ptr, cast_ptr, proc) \
|
||||
switch (dtype) { \
|
||||
case xgboost::DataType::kFloat32: { \
|
||||
auto cast_ptr = reinterpret_cast<const float*>(old_ptr); proc; break; \
|
||||
} \
|
||||
case xgboost::DataType::kDouble: { \
|
||||
auto cast_ptr = reinterpret_cast<const double*>(old_ptr); proc; break; \
|
||||
} \
|
||||
case xgboost::DataType::kUInt32: { \
|
||||
auto cast_ptr = reinterpret_cast<const uint32_t*>(old_ptr); proc; break; \
|
||||
} \
|
||||
case xgboost::DataType::kUInt64: { \
|
||||
auto cast_ptr = reinterpret_cast<const uint64_t*>(old_ptr); proc; break; \
|
||||
} \
|
||||
default: LOG(FATAL) << "Unknown data type" << static_cast<uint8_t>(dtype); \
|
||||
} \
|
||||
|
||||
void MetaInfo::SetInfo(const char* key, const void* dptr, DataType dtype, size_t num) {
|
||||
if (!std::strcmp(key, "label")) {
|
||||
auto& labels = labels_.HostVector();
|
||||
labels.resize(num);
|
||||
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
|
||||
std::copy(cast_dptr, cast_dptr + num, labels.begin()));
|
||||
auto valid = std::none_of(labels.cbegin(), labels.cend(), [](auto y) {
|
||||
return std::isnan(y) || std::isinf(y);
|
||||
});
|
||||
CHECK(valid) << "Label contains NaN, infinity or a value too large.";
|
||||
} else if (!std::strcmp(key, "weight")) {
|
||||
auto& weights = weights_.HostVector();
|
||||
weights.resize(num);
|
||||
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
|
||||
std::copy(cast_dptr, cast_dptr + num, weights.begin()));
|
||||
auto valid = std::none_of(weights.cbegin(), weights.cend(), [](float w) {
|
||||
return w < 0 || std::isinf(w) || std::isnan(w);
|
||||
});
|
||||
CHECK(valid) << "Weights must be positive values.";
|
||||
} else if (!std::strcmp(key, "base_margin")) {
|
||||
auto& base_margin = base_margin_.HostVector();
|
||||
base_margin.resize(num);
|
||||
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
|
||||
std::copy(cast_dptr, cast_dptr + num, base_margin.begin()));
|
||||
} else if (!std::strcmp(key, "group")) {
|
||||
group_ptr_.clear(); group_ptr_.resize(num + 1, 0);
|
||||
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
|
||||
std::copy(cast_dptr, cast_dptr + num, group_ptr_.begin() + 1));
|
||||
group_ptr_[0] = 0;
|
||||
for (size_t i = 1; i < group_ptr_.size(); ++i) {
|
||||
group_ptr_[i] = group_ptr_[i - 1] + group_ptr_[i];
|
||||
namespace {
|
||||
template <int32_t D, typename T>
|
||||
void CopyTensorInfoImpl(Json arr_interface, linalg::Tensor<T, D>* p_out) {
|
||||
ArrayInterface<D> array{arr_interface};
|
||||
if (array.n == 0) {
|
||||
return;
|
||||
}
|
||||
CHECK(array.valid.Size() == 0) << "Meta info like label or weight can not have missing value.";
|
||||
if (array.is_contiguous && array.type == ToDType<T>::kType) {
|
||||
// Handle contigious
|
||||
p_out->ModifyInplace([&](HostDeviceVector<T>* data, common::Span<size_t, D> shape) {
|
||||
// set shape
|
||||
std::copy(array.shape, array.shape + D, shape.data());
|
||||
// set data
|
||||
data->Resize(array.n);
|
||||
std::memcpy(data->HostPointer(), array.data, array.n * sizeof(T));
|
||||
});
|
||||
return;
|
||||
}
|
||||
p_out->Reshape(array.shape);
|
||||
auto t = p_out->View(GenericParameter::kCpuId);
|
||||
CHECK(t.Contiguous());
|
||||
// FIXME(jiamingy): Remove the use of this default thread.
|
||||
linalg::ElementWiseKernelHost(t, common::OmpGetNumThreads(0), [&](auto i, auto) {
|
||||
return linalg::detail::Apply(TypedIndex<T, D>{array}, linalg::UnravelIndex<D>(i, t.Shape()));
|
||||
});
|
||||
}
|
||||
} // namespace
|
||||
|
||||
void MetaInfo::SetInfo(StringView key, StringView interface_str) {
|
||||
Json j_interface = Json::Load(interface_str);
|
||||
bool is_cuda{false};
|
||||
if (IsA<Array>(j_interface)) {
|
||||
auto const& array = get<Array const>(j_interface);
|
||||
CHECK_GE(array.size(), 0) << "Invalid " << key
|
||||
<< ", must have at least 1 column even if it's empty.";
|
||||
auto const& first = get<Object const>(array.front());
|
||||
auto ptr = ArrayInterfaceHandler::GetPtrFromArrayData<void*>(first);
|
||||
is_cuda = ArrayInterfaceHandler::IsCudaPtr(ptr);
|
||||
} else {
|
||||
auto const& first = get<Object const>(j_interface);
|
||||
auto ptr = ArrayInterfaceHandler::GetPtrFromArrayData<void*>(first);
|
||||
is_cuda = ArrayInterfaceHandler::IsCudaPtr(ptr);
|
||||
}
|
||||
|
||||
if (is_cuda) {
|
||||
this->SetInfoFromCUDA(key, j_interface);
|
||||
} else {
|
||||
this->SetInfoFromHost(key, j_interface);
|
||||
}
|
||||
}
|
||||
|
||||
void MetaInfo::SetInfoFromHost(StringView key, Json arr) {
|
||||
// multi-dim float info
|
||||
if (key == "base_margin") {
|
||||
CopyTensorInfoImpl<3>(arr, &this->base_margin_);
|
||||
// FIXME(jiamingy): Remove the deprecated API and let all language bindings aware of
|
||||
// input shape. This issue is CPU only since CUDA uses array interface from day 1.
|
||||
//
|
||||
// Python binding always understand the shape, so this condition should not occur for
|
||||
// it.
|
||||
if (this->num_row_ != 0 && this->base_margin_.Shape(0) != this->num_row_) {
|
||||
// API functions that don't use array interface don't understand shape.
|
||||
CHECK(this->base_margin_.Size() % this->num_row_ == 0) << "Incorrect size for base margin.";
|
||||
size_t n_groups = this->base_margin_.Size() / this->num_row_;
|
||||
this->base_margin_.Reshape(this->num_row_, n_groups);
|
||||
}
|
||||
return;
|
||||
}
|
||||
// uint info
|
||||
if (key == "group") {
|
||||
linalg::Tensor<bst_group_t, 1> t;
|
||||
CopyTensorInfoImpl(arr, &t);
|
||||
auto const& h_groups = t.Data()->HostVector();
|
||||
group_ptr_.clear();
|
||||
group_ptr_.resize(h_groups.size() + 1, 0);
|
||||
group_ptr_[0] = 0;
|
||||
std::partial_sum(h_groups.cbegin(), h_groups.cend(), group_ptr_.begin() + 1);
|
||||
data::ValidateQueryGroup(group_ptr_);
|
||||
} else if (!std::strcmp(key, "qid")) {
|
||||
std::vector<uint32_t> query_ids(num, 0);
|
||||
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
|
||||
std::copy(cast_dptr, cast_dptr + num, query_ids.begin()));
|
||||
return;
|
||||
} else if (key == "qid") {
|
||||
linalg::Tensor<bst_group_t, 1> t;
|
||||
CopyTensorInfoImpl(arr, &t);
|
||||
bool non_dec = true;
|
||||
auto const& query_ids = t.Data()->HostVector();
|
||||
for (size_t i = 1; i < query_ids.size(); ++i) {
|
||||
if (query_ids[i] < query_ids[i - 1]) {
|
||||
non_dec = false;
|
||||
@ -401,7 +484,8 @@ void MetaInfo::SetInfo(const char* key, const void* dptr, DataType dtype, size_t
|
||||
}
|
||||
}
|
||||
CHECK(non_dec) << "`qid` must be sorted in non-decreasing order along with data.";
|
||||
group_ptr_.clear(); group_ptr_.push_back(0);
|
||||
group_ptr_.clear();
|
||||
group_ptr_.push_back(0);
|
||||
for (size_t i = 1; i < query_ids.size(); ++i) {
|
||||
if (query_ids[i] != query_ids[i - 1]) {
|
||||
group_ptr_.push_back(i);
|
||||
@ -410,31 +494,75 @@ void MetaInfo::SetInfo(const char* key, const void* dptr, DataType dtype, size_t
|
||||
if (group_ptr_.back() != query_ids.size()) {
|
||||
group_ptr_.push_back(query_ids.size());
|
||||
}
|
||||
} else if (!std::strcmp(key, "label_lower_bound")) {
|
||||
auto& labels = labels_lower_bound_.HostVector();
|
||||
labels.resize(num);
|
||||
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
|
||||
std::copy(cast_dptr, cast_dptr + num, labels.begin()));
|
||||
} else if (!std::strcmp(key, "label_upper_bound")) {
|
||||
auto& labels = labels_upper_bound_.HostVector();
|
||||
labels.resize(num);
|
||||
DISPATCH_CONST_PTR(dtype, dptr, cast_dptr,
|
||||
std::copy(cast_dptr, cast_dptr + num, labels.begin()));
|
||||
} else if (!std::strcmp(key, "feature_weights")) {
|
||||
auto &h_feature_weights = feature_weights.HostVector();
|
||||
h_feature_weights.resize(num);
|
||||
DISPATCH_CONST_PTR(
|
||||
dtype, dptr, cast_dptr,
|
||||
std::copy(cast_dptr, cast_dptr + num, h_feature_weights.begin()));
|
||||
data::ValidateQueryGroup(group_ptr_);
|
||||
return;
|
||||
}
|
||||
// float info
|
||||
linalg::Tensor<float, 1> t;
|
||||
CopyTensorInfoImpl<1>(arr, &t);
|
||||
if (key == "label") {
|
||||
this->labels_ = std::move(*t.Data());
|
||||
auto const& h_labels = labels_.ConstHostVector();
|
||||
auto valid = std::none_of(h_labels.cbegin(), h_labels.cend(), data::LabelsCheck{});
|
||||
CHECK(valid) << "Label contains NaN, infinity or a value too large.";
|
||||
} else if (key == "weight") {
|
||||
this->weights_ = std::move(*t.Data());
|
||||
auto const& h_weights = this->weights_.ConstHostVector();
|
||||
auto valid = std::none_of(h_weights.cbegin(), h_weights.cend(),
|
||||
[](float w) { return w < 0 || std::isinf(w) || std::isnan(w); });
|
||||
CHECK(valid) << "Weights must be positive values.";
|
||||
} else if (key == "label_lower_bound") {
|
||||
this->labels_lower_bound_ = std::move(*t.Data());
|
||||
} else if (key == "label_upper_bound") {
|
||||
this->labels_upper_bound_ = std::move(*t.Data());
|
||||
} else if (key == "feature_weights") {
|
||||
this->feature_weights = std::move(*t.Data());
|
||||
auto const& h_feature_weights = feature_weights.ConstHostVector();
|
||||
bool valid =
|
||||
std::none_of(h_feature_weights.cbegin(), h_feature_weights.cend(),
|
||||
[](float w) { return w < 0; });
|
||||
std::none_of(h_feature_weights.cbegin(), h_feature_weights.cend(), data::WeightsCheck{});
|
||||
CHECK(valid) << "Feature weight must be greater than 0.";
|
||||
} else {
|
||||
LOG(FATAL) << "Unknown key for MetaInfo: " << key;
|
||||
}
|
||||
}
|
||||
|
||||
void MetaInfo::SetInfo(const char* key, const void* dptr, DataType dtype, size_t num) {
|
||||
auto proc = [&](auto cast_d_ptr) {
|
||||
using T = std::remove_pointer_t<decltype(cast_d_ptr)>;
|
||||
auto t =
|
||||
linalg::TensorView<T, 1>(common::Span<T>{cast_d_ptr, num}, {num}, GenericParameter::kCpuId);
|
||||
CHECK(t.Contiguous());
|
||||
Json interface { t.ArrayInterface() };
|
||||
assert(ArrayInterface<1>{interface}.is_contiguous);
|
||||
return interface;
|
||||
};
|
||||
// Legacy code using XGBoost dtype, which is a small subset of array interface types.
|
||||
switch (dtype) {
|
||||
case xgboost::DataType::kFloat32: {
|
||||
auto cast_ptr = reinterpret_cast<const float*>(dptr);
|
||||
this->SetInfoFromHost(key, proc(cast_ptr));
|
||||
break;
|
||||
}
|
||||
case xgboost::DataType::kDouble: {
|
||||
auto cast_ptr = reinterpret_cast<const double*>(dptr);
|
||||
this->SetInfoFromHost(key, proc(cast_ptr));
|
||||
break;
|
||||
}
|
||||
case xgboost::DataType::kUInt32: {
|
||||
auto cast_ptr = reinterpret_cast<const uint32_t*>(dptr);
|
||||
this->SetInfoFromHost(key, proc(cast_ptr));
|
||||
break;
|
||||
}
|
||||
case xgboost::DataType::kUInt64: {
|
||||
auto cast_ptr = reinterpret_cast<const uint64_t*>(dptr);
|
||||
this->SetInfoFromHost(key, proc(cast_ptr));
|
||||
break;
|
||||
}
|
||||
default:
|
||||
LOG(FATAL) << "Unknown data type" << static_cast<uint8_t>(dtype);
|
||||
}
|
||||
}
|
||||
|
||||
void MetaInfo::GetInfo(char const* key, bst_ulong* out_len, DataType dtype,
|
||||
const void** out_dptr) const {
|
||||
if (dtype == DataType::kFloat32) {
|
||||
@ -444,7 +572,7 @@ void MetaInfo::GetInfo(char const *key, bst_ulong *out_len, DataType dtype,
|
||||
} else if (!std::strcmp(key, "weight")) {
|
||||
vec = &this->weights_.HostVector();
|
||||
} else if (!std::strcmp(key, "base_margin")) {
|
||||
vec = &this->base_margin_.HostVector();
|
||||
vec = &this->base_margin_.Data()->HostVector();
|
||||
} else if (!std::strcmp(key, "label_lower_bound")) {
|
||||
vec = &this->labels_lower_bound_.HostVector();
|
||||
} else if (!std::strcmp(key, "label_upper_bound")) {
|
||||
@ -533,8 +661,7 @@ void MetaInfo::Extend(MetaInfo const& that, bool accumulate_rows, bool check_col
|
||||
this->labels_upper_bound_.SetDevice(that.labels_upper_bound_.DeviceIdx());
|
||||
this->labels_upper_bound_.Extend(that.labels_upper_bound_);
|
||||
|
||||
this->base_margin_.SetDevice(that.base_margin_.DeviceIdx());
|
||||
this->base_margin_.Extend(that.base_margin_);
|
||||
linalg::Stack(&this->base_margin_, that.base_margin_);
|
||||
|
||||
if (this->group_ptr_.size() == 0) {
|
||||
this->group_ptr_ = that.group_ptr_;
|
||||
@ -617,14 +744,12 @@ void MetaInfo::Validate(int32_t device) const {
|
||||
if (base_margin_.Size() != 0) {
|
||||
CHECK_EQ(base_margin_.Size() % num_row_, 0)
|
||||
<< "Size of base margin must be a multiple of number of rows.";
|
||||
check_device(base_margin_);
|
||||
check_device(*base_margin_.Data());
|
||||
}
|
||||
}
|
||||
|
||||
#if !defined(XGBOOST_USE_CUDA)
|
||||
void MetaInfo::SetInfo(StringView key, std::string const& interface_str) {
|
||||
common::AssertGPUSupport();
|
||||
}
|
||||
void MetaInfo::SetInfoFromCUDA(StringView key, Json arr) { common::AssertGPUSupport(); }
|
||||
#endif // !defined(XGBOOST_USE_CUDA)
|
||||
|
||||
using DMatrixThreadLocal =
|
||||
@ -778,10 +903,10 @@ DMatrix* DMatrix::Load(const std::string& uri,
|
||||
LOG(CONSOLE) << info.group_ptr_.size() - 1
|
||||
<< " groups are loaded from " << fname << ".group";
|
||||
}
|
||||
if (MetaTryLoadFloatInfo
|
||||
(fname + ".base_margin", &info.base_margin_.HostVector()) && !silent) {
|
||||
LOG(CONSOLE) << info.base_margin_.Size()
|
||||
<< " base_margin are loaded from " << fname << ".base_margin";
|
||||
if (MetaTryLoadFloatInfo(fname + ".base_margin", &info.base_margin_.Data()->HostVector()) &&
|
||||
!silent) {
|
||||
LOG(CONSOLE) << info.base_margin_.Size() << " base_margin are loaded from " << fname
|
||||
<< ".base_margin";
|
||||
}
|
||||
if (MetaTryLoadFloatInfo
|
||||
(fname + ".weight", &info.weights_.HostVector()) && !silent) {
|
||||
|
||||
@ -114,14 +114,10 @@ void CopyQidImpl(ArrayInterface<1> array_interface, std::vector<bst_group_t>* p_
|
||||
}
|
||||
} // namespace
|
||||
|
||||
void MetaInfo::SetInfo(StringView key, std::string const& interface_str) {
|
||||
Json array = Json::Load(StringView{interface_str});
|
||||
void MetaInfo::SetInfoFromCUDA(StringView key, Json array) {
|
||||
// multi-dim float info
|
||||
if (key == "base_margin") {
|
||||
// FIXME(jiamingy): This is temporary until #7405 can be fully merged
|
||||
linalg::Tensor<float, 3> t;
|
||||
CopyTensorInfoImpl(array, &t);
|
||||
base_margin_ = std::move(*t.Data());
|
||||
CopyTensorInfoImpl(array, &base_margin_);
|
||||
return;
|
||||
}
|
||||
// uint info
|
||||
|
||||
@ -137,9 +137,10 @@ SimpleDMatrix::SimpleDMatrix(AdapterT* adapter, float missing, int nthread) {
|
||||
batch.Weights() + batch.Size());
|
||||
}
|
||||
if (batch.BaseMargin() != nullptr) {
|
||||
auto& base_margin = info_.base_margin_.HostVector();
|
||||
base_margin.insert(base_margin.end(), batch.BaseMargin(),
|
||||
batch.BaseMargin() + batch.Size());
|
||||
info_.base_margin_ = linalg::Tensor<float, 3>{batch.BaseMargin(),
|
||||
batch.BaseMargin() + batch.Size(),
|
||||
{batch.Size()},
|
||||
GenericParameter::kCpuId};
|
||||
}
|
||||
if (batch.Qid() != nullptr) {
|
||||
qids.insert(qids.end(), batch.Qid(), batch.Qid() + batch.Size());
|
||||
|
||||
@ -178,7 +178,7 @@ class GBLinear : public GradientBooster {
|
||||
unsigned layer_begin, unsigned layer_end, bool, int, unsigned) override {
|
||||
model_.LazyInitModel();
|
||||
LinearCheckLayer(layer_begin, layer_end);
|
||||
const auto& base_margin = p_fmat->Info().base_margin_.ConstHostVector();
|
||||
auto base_margin = p_fmat->Info().base_margin_.View(GenericParameter::kCpuId);
|
||||
const int ngroup = model_.learner_model_param->num_output_group;
|
||||
const size_t ncolumns = model_.learner_model_param->num_feature + 1;
|
||||
// allocate space for (#features + bias) times #groups times #rows
|
||||
@ -203,9 +203,9 @@ class GBLinear : public GradientBooster {
|
||||
p_contribs[ins.index] = ins.fvalue * model_[ins.index][gid];
|
||||
}
|
||||
// add base margin to BIAS
|
||||
p_contribs[ncolumns - 1] = model_.Bias()[gid] +
|
||||
((base_margin.size() != 0) ? base_margin[row_idx * ngroup + gid] :
|
||||
learner_model_param_->base_score);
|
||||
p_contribs[ncolumns - 1] =
|
||||
model_.Bias()[gid] + ((base_margin.Size() != 0) ? base_margin(row_idx, gid)
|
||||
: learner_model_param_->base_score);
|
||||
}
|
||||
});
|
||||
}
|
||||
@ -270,7 +270,7 @@ class GBLinear : public GradientBooster {
|
||||
monitor_.Start("PredictBatchInternal");
|
||||
model_.LazyInitModel();
|
||||
std::vector<bst_float> &preds = *out_preds;
|
||||
const auto& base_margin = p_fmat->Info().base_margin_.ConstHostVector();
|
||||
auto base_margin = p_fmat->Info().base_margin_.View(GenericParameter::kCpuId);
|
||||
// start collecting the prediction
|
||||
const int ngroup = model_.learner_model_param->num_output_group;
|
||||
preds.resize(p_fmat->Info().num_row_ * ngroup);
|
||||
@ -280,16 +280,15 @@ class GBLinear : public GradientBooster {
|
||||
// k is number of group
|
||||
// parallel over local batch
|
||||
const auto nsize = static_cast<omp_ulong>(batch.Size());
|
||||
if (base_margin.size() != 0) {
|
||||
CHECK_EQ(base_margin.size(), nsize * ngroup);
|
||||
if (base_margin.Size() != 0) {
|
||||
CHECK_EQ(base_margin.Size(), nsize * ngroup);
|
||||
}
|
||||
common::ParallelFor(nsize, [&](omp_ulong i) {
|
||||
const size_t ridx = page.base_rowid + i;
|
||||
// loop over output groups
|
||||
for (int gid = 0; gid < ngroup; ++gid) {
|
||||
bst_float margin =
|
||||
(base_margin.size() != 0) ?
|
||||
base_margin[ridx * ngroup + gid] : learner_model_param_->base_score;
|
||||
float margin =
|
||||
(base_margin.Size() != 0) ? base_margin(ridx, gid) : learner_model_param_->base_score;
|
||||
this->Pred(batch[i], &preds[ridx * ngroup], gid, margin);
|
||||
}
|
||||
});
|
||||
|
||||
@ -282,27 +282,6 @@ class CPUPredictor : public Predictor {
|
||||
}
|
||||
}
|
||||
|
||||
void InitOutPredictions(const MetaInfo& info,
|
||||
HostDeviceVector<bst_float>* out_preds,
|
||||
const gbm::GBTreeModel& model) const override {
|
||||
CHECK_NE(model.learner_model_param->num_output_group, 0);
|
||||
size_t n = model.learner_model_param->num_output_group * info.num_row_;
|
||||
const auto& base_margin = info.base_margin_.HostVector();
|
||||
out_preds->Resize(n);
|
||||
std::vector<bst_float>& out_preds_h = out_preds->HostVector();
|
||||
if (base_margin.empty()) {
|
||||
std::fill(out_preds_h.begin(), out_preds_h.end(),
|
||||
model.learner_model_param->base_score);
|
||||
} else {
|
||||
std::string expected{
|
||||
"(" + std::to_string(info.num_row_) + ", " +
|
||||
std::to_string(model.learner_model_param->num_output_group) + ")"};
|
||||
CHECK_EQ(base_margin.size(), n)
|
||||
<< "Invalid shape of base_margin. Expected:" << expected;
|
||||
std::copy(base_margin.begin(), base_margin.end(), out_preds_h.begin());
|
||||
}
|
||||
}
|
||||
|
||||
public:
|
||||
explicit CPUPredictor(GenericParameter const* generic_param) :
|
||||
Predictor::Predictor{generic_param} {}
|
||||
@ -456,7 +435,7 @@ class CPUPredictor : public Predictor {
|
||||
common::ParallelFor(bst_omp_uint(ntree_limit), [&](bst_omp_uint i) {
|
||||
FillNodeMeanValues(model.trees[i].get(), &(mean_values[i]));
|
||||
});
|
||||
const std::vector<bst_float>& base_margin = info.base_margin_.HostVector();
|
||||
auto base_margin = info.base_margin_.View(GenericParameter::kCpuId);
|
||||
// start collecting the contributions
|
||||
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
|
||||
auto page = batch.GetView();
|
||||
@ -496,8 +475,9 @@ class CPUPredictor : public Predictor {
|
||||
}
|
||||
feats.Drop(page[i]);
|
||||
// add base margin to BIAS
|
||||
if (base_margin.size() != 0) {
|
||||
p_contribs[ncolumns - 1] += base_margin[row_idx * ngroup + gid];
|
||||
if (base_margin.Size() != 0) {
|
||||
CHECK_EQ(base_margin.Shape(1), ngroup);
|
||||
p_contribs[ncolumns - 1] += base_margin(row_idx, gid);
|
||||
} else {
|
||||
p_contribs[ncolumns - 1] += model.learner_model_param->base_score;
|
||||
}
|
||||
|
||||
@ -855,7 +855,7 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
}
|
||||
// Add the base margin term to last column
|
||||
p_fmat->Info().base_margin_.SetDevice(generic_param_->gpu_id);
|
||||
const auto margin = p_fmat->Info().base_margin_.ConstDeviceSpan();
|
||||
const auto margin = p_fmat->Info().base_margin_.Data()->ConstDeviceSpan();
|
||||
float base_score = model.learner_model_param->base_score;
|
||||
dh::LaunchN(
|
||||
p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
|
||||
@ -914,7 +914,7 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
}
|
||||
// Add the base margin term to last column
|
||||
p_fmat->Info().base_margin_.SetDevice(generic_param_->gpu_id);
|
||||
const auto margin = p_fmat->Info().base_margin_.ConstDeviceSpan();
|
||||
const auto margin = p_fmat->Info().base_margin_.Data()->ConstDeviceSpan();
|
||||
float base_score = model.learner_model_param->base_score;
|
||||
size_t n_features = model.learner_model_param->num_feature;
|
||||
dh::LaunchN(
|
||||
@ -928,27 +928,6 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
});
|
||||
}
|
||||
|
||||
protected:
|
||||
void InitOutPredictions(const MetaInfo& info,
|
||||
HostDeviceVector<bst_float>* out_preds,
|
||||
const gbm::GBTreeModel& model) const override {
|
||||
size_t n_classes = model.learner_model_param->num_output_group;
|
||||
size_t n = n_classes * info.num_row_;
|
||||
const HostDeviceVector<bst_float>& base_margin = info.base_margin_;
|
||||
out_preds->SetDevice(generic_param_->gpu_id);
|
||||
out_preds->Resize(n);
|
||||
if (base_margin.Size() != 0) {
|
||||
std::string expected{
|
||||
"(" + std::to_string(info.num_row_) + ", " +
|
||||
std::to_string(model.learner_model_param->num_output_group) + ")"};
|
||||
CHECK_EQ(base_margin.Size(), n)
|
||||
<< "Invalid shape of base_margin. Expected:" << expected;
|
||||
out_preds->Copy(base_margin);
|
||||
} else {
|
||||
out_preds->Fill(model.learner_model_param->base_score);
|
||||
}
|
||||
}
|
||||
|
||||
void PredictInstance(const SparsePage::Inst&,
|
||||
std::vector<bst_float>*,
|
||||
const gbm::GBTreeModel&, unsigned) const override {
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
/*!
|
||||
* Copyright 2017-2020 by Contributors
|
||||
* Copyright 2017-2021 by Contributors
|
||||
*/
|
||||
#include <dmlc/registry.h>
|
||||
#include <mutex>
|
||||
@ -8,6 +8,8 @@
|
||||
#include "xgboost/data.h"
|
||||
#include "xgboost/generic_parameters.h"
|
||||
|
||||
#include "../gbm/gbtree.h"
|
||||
|
||||
namespace dmlc {
|
||||
DMLC_REGISTRY_ENABLE(::xgboost::PredictorReg);
|
||||
} // namespace dmlc
|
||||
@ -58,6 +60,38 @@ Predictor* Predictor::Create(
|
||||
auto p_predictor = (e->body)(generic_param);
|
||||
return p_predictor;
|
||||
}
|
||||
|
||||
void ValidateBaseMarginShape(linalg::Tensor<float, 3> const& margin, bst_row_t n_samples,
|
||||
bst_group_t n_groups) {
|
||||
// FIXME: Bindings other than Python doesn't have shape.
|
||||
std::string expected{"Invalid shape of base_margin. Expected: (" + std::to_string(n_samples) +
|
||||
", " + std::to_string(n_groups) + ")"};
|
||||
CHECK_EQ(margin.Shape(0), n_samples) << expected;
|
||||
CHECK_EQ(margin.Shape(1), n_groups) << expected;
|
||||
}
|
||||
|
||||
void Predictor::InitOutPredictions(const MetaInfo& info, HostDeviceVector<bst_float>* out_preds,
|
||||
const gbm::GBTreeModel& model) const {
|
||||
CHECK_NE(model.learner_model_param->num_output_group, 0);
|
||||
size_t n_classes = model.learner_model_param->num_output_group;
|
||||
size_t n = n_classes * info.num_row_;
|
||||
const HostDeviceVector<bst_float>* base_margin = info.base_margin_.Data();
|
||||
if (generic_param_->gpu_id >= 0) {
|
||||
out_preds->SetDevice(generic_param_->gpu_id);
|
||||
}
|
||||
if (base_margin->Size() != 0) {
|
||||
out_preds->Resize(n);
|
||||
ValidateBaseMarginShape(info.base_margin_, info.num_row_, n_classes);
|
||||
out_preds->Copy(*base_margin);
|
||||
} else {
|
||||
if (out_preds->Empty()) {
|
||||
out_preds->Resize(n, model.learner_model_param->base_score);
|
||||
} else {
|
||||
out_preds->Resize(n);
|
||||
out_preds->Fill(model.learner_model_param->base_score);
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace xgboost
|
||||
|
||||
namespace xgboost {
|
||||
|
||||
@ -57,7 +57,7 @@ inline data::CupyAdapter AdapterFromData(const thrust::device_vector<float> &x,
|
||||
Json(Integer(reinterpret_cast<Integer::Int>(x.data().get()))),
|
||||
Json(Boolean(false))};
|
||||
array_interface["data"] = j_data;
|
||||
array_interface["version"] = Integer(static_cast<Integer::Int>(1));
|
||||
array_interface["version"] = 3;
|
||||
array_interface["typestr"] = String("<f4");
|
||||
std::string str;
|
||||
Json::Dump(array_interface, &str);
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
// Copyright 2016-2020 by Contributors
|
||||
// Copyright 2016-2021 by Contributors
|
||||
#include "test_metainfo.h"
|
||||
|
||||
#include <dmlc/io.h>
|
||||
#include <dmlc/filesystem.h>
|
||||
#include <xgboost/data.h>
|
||||
@ -122,7 +124,10 @@ TEST(MetaInfo, SaveLoadBinary) {
|
||||
EXPECT_EQ(inforead.labels_.HostVector(), info.labels_.HostVector());
|
||||
EXPECT_EQ(inforead.group_ptr_, info.group_ptr_);
|
||||
EXPECT_EQ(inforead.weights_.HostVector(), info.weights_.HostVector());
|
||||
EXPECT_EQ(inforead.base_margin_.HostVector(), info.base_margin_.HostVector());
|
||||
|
||||
auto orig_margin = info.base_margin_.View(xgboost::GenericParameter::kCpuId);
|
||||
auto read_margin = inforead.base_margin_.View(xgboost::GenericParameter::kCpuId);
|
||||
EXPECT_TRUE(std::equal(orig_margin.cbegin(), orig_margin.cend(), read_margin.cbegin()));
|
||||
|
||||
EXPECT_EQ(inforead.feature_type_names.size(), kCols);
|
||||
EXPECT_EQ(inforead.feature_types.Size(), kCols);
|
||||
@ -254,10 +259,10 @@ TEST(MetaInfo, Validate) {
|
||||
xgboost::HostDeviceVector<xgboost::bst_group_t> d_groups{groups};
|
||||
d_groups.SetDevice(0);
|
||||
d_groups.DevicePointer(); // pull to device
|
||||
auto arr_interface = xgboost::GetArrayInterface(&d_groups, 64, 1);
|
||||
std::string arr_interface_str;
|
||||
xgboost::Json::Dump(arr_interface, &arr_interface_str);
|
||||
EXPECT_THROW(info.SetInfo("group", arr_interface_str), dmlc::Error);
|
||||
std::string arr_interface_str{
|
||||
xgboost::linalg::MakeVec(d_groups.ConstDevicePointer(), d_groups.Size(), 0)
|
||||
.ArrayInterfaceStr()};
|
||||
EXPECT_THROW(info.SetInfo("group", xgboost::StringView{arr_interface_str}), dmlc::Error);
|
||||
#endif // defined(XGBOOST_USE_CUDA)
|
||||
}
|
||||
|
||||
@ -292,3 +297,7 @@ TEST(MetaInfo, HostExtend) {
|
||||
ASSERT_EQ(lhs.group_ptr_.at(i), per_group * i);
|
||||
}
|
||||
}
|
||||
|
||||
namespace xgboost {
|
||||
TEST(MetaInfo, CPUStridedData) { TestMetaInfoStridedData(GenericParameter::kCpuId); }
|
||||
} // namespace xgboost
|
||||
|
||||
@ -3,10 +3,13 @@
|
||||
#include <gtest/gtest.h>
|
||||
#include <xgboost/data.h>
|
||||
#include <xgboost/json.h>
|
||||
#include <xgboost/generic_parameters.h>
|
||||
#include <thrust/device_vector.h>
|
||||
#include "test_array_interface.h"
|
||||
#include "../../../src/common/device_helpers.cuh"
|
||||
|
||||
#include "test_metainfo.h"
|
||||
|
||||
namespace xgboost {
|
||||
|
||||
template <typename T>
|
||||
@ -23,7 +26,7 @@ std::string PrepareData(std::string typestr, thrust::device_vector<T>* out, cons
|
||||
std::vector<Json> j_shape {Json(Integer(static_cast<Integer::Int>(kRows)))};
|
||||
column["shape"] = Array(j_shape);
|
||||
column["strides"] = Array(std::vector<Json>{Json(Integer(static_cast<Integer::Int>(sizeof(T))))});
|
||||
column["version"] = Integer(static_cast<Integer::Int>(1));
|
||||
column["version"] = 3;
|
||||
column["typestr"] = String(typestr);
|
||||
|
||||
auto p_d_data = d_data.data().get();
|
||||
@ -31,6 +34,7 @@ std::string PrepareData(std::string typestr, thrust::device_vector<T>* out, cons
|
||||
Json(Integer(reinterpret_cast<Integer::Int>(p_d_data))),
|
||||
Json(Boolean(false))};
|
||||
column["data"] = j_data;
|
||||
column["stream"] = nullptr;
|
||||
Json array(std::vector<Json>{column});
|
||||
|
||||
std::string str;
|
||||
@ -49,6 +53,7 @@ TEST(MetaInfo, FromInterface) {
|
||||
info.SetInfo("label", str.c_str());
|
||||
|
||||
auto const& h_label = info.labels_.HostVector();
|
||||
ASSERT_EQ(h_label.size(), d_data.size());
|
||||
for (size_t i = 0; i < d_data.size(); ++i) {
|
||||
ASSERT_EQ(h_label[i], d_data[i]);
|
||||
}
|
||||
@ -60,9 +65,10 @@ TEST(MetaInfo, FromInterface) {
|
||||
}
|
||||
|
||||
info.SetInfo("base_margin", str.c_str());
|
||||
auto const& h_base_margin = info.base_margin_.HostVector();
|
||||
auto const h_base_margin = info.base_margin_.View(GenericParameter::kCpuId);
|
||||
ASSERT_EQ(h_base_margin.Size(), d_data.size());
|
||||
for (size_t i = 0; i < d_data.size(); ++i) {
|
||||
ASSERT_EQ(h_base_margin[i], d_data[i]);
|
||||
ASSERT_EQ(h_base_margin(i), d_data[i]);
|
||||
}
|
||||
|
||||
thrust::device_vector<int> d_group_data;
|
||||
@ -76,6 +82,10 @@ TEST(MetaInfo, FromInterface) {
|
||||
EXPECT_EQ(info.group_ptr_, expected_group_ptr);
|
||||
}
|
||||
|
||||
TEST(MetaInfo, GPUStridedData) {
|
||||
TestMetaInfoStridedData(0);
|
||||
}
|
||||
|
||||
TEST(MetaInfo, Group) {
|
||||
cudaSetDevice(0);
|
||||
MetaInfo info;
|
||||
|
||||
82
tests/cpp/data/test_metainfo.h
Normal file
82
tests/cpp/data/test_metainfo.h
Normal file
@ -0,0 +1,82 @@
|
||||
/*!
|
||||
* Copyright 2021 by XGBoost Contributors
|
||||
*/
|
||||
#ifndef XGBOOST_TESTS_CPP_DATA_TEST_METAINFO_H_
|
||||
#define XGBOOST_TESTS_CPP_DATA_TEST_METAINFO_H_
|
||||
#include <gtest/gtest.h>
|
||||
#include <xgboost/data.h>
|
||||
#include <xgboost/host_device_vector.h>
|
||||
#include <xgboost/linalg.h>
|
||||
|
||||
#include <numeric>
|
||||
#include "../../../src/data/array_interface.h"
|
||||
#include "../../../src/common/linalg_op.h"
|
||||
|
||||
namespace xgboost {
|
||||
inline void TestMetaInfoStridedData(int32_t device) {
|
||||
MetaInfo info;
|
||||
{
|
||||
// label
|
||||
HostDeviceVector<float> labels;
|
||||
labels.Resize(64);
|
||||
auto& h_labels = labels.HostVector();
|
||||
std::iota(h_labels.begin(), h_labels.end(), 0.0f);
|
||||
bool is_gpu = device >= 0;
|
||||
if (is_gpu) {
|
||||
labels.SetDevice(0);
|
||||
}
|
||||
|
||||
auto t = linalg::TensorView<float const, 2>{
|
||||
is_gpu ? labels.ConstDeviceSpan() : labels.ConstHostSpan(), {32, 2}, device};
|
||||
auto s = t.Slice(linalg::All(), 0);
|
||||
|
||||
auto str = s.ArrayInterfaceStr();
|
||||
ASSERT_EQ(s.Size(), 32);
|
||||
|
||||
info.SetInfo("label", StringView{str});
|
||||
auto const& h_result = info.labels_.HostVector();
|
||||
ASSERT_EQ(h_result.size(), 32);
|
||||
|
||||
for (auto v : h_result) {
|
||||
ASSERT_EQ(static_cast<int32_t>(v) % 2, 0);
|
||||
}
|
||||
}
|
||||
{
|
||||
// qid
|
||||
linalg::Tensor<uint64_t, 2> qid;
|
||||
qid.Reshape(32, 2);
|
||||
auto& h_qid = qid.Data()->HostVector();
|
||||
std::iota(h_qid.begin(), h_qid.end(), 0);
|
||||
auto s = qid.View(device).Slice(linalg::All(), 0);
|
||||
auto str = s.ArrayInterfaceStr();
|
||||
info.SetInfo("qid", StringView{str});
|
||||
auto const& h_result = info.group_ptr_;
|
||||
ASSERT_EQ(h_result.size(), s.Size() + 1);
|
||||
}
|
||||
{
|
||||
// base margin
|
||||
linalg::Tensor<float, 4> base_margin;
|
||||
base_margin.Reshape(4, 3, 2, 3);
|
||||
auto& h_margin = base_margin.Data()->HostVector();
|
||||
std::iota(h_margin.begin(), h_margin.end(), 0.0);
|
||||
auto t_margin = base_margin.View(device).Slice(linalg::All(), linalg::All(), 0, linalg::All());
|
||||
ASSERT_EQ(t_margin.Shape().size(), 3);
|
||||
|
||||
info.SetInfo("base_margin", StringView{t_margin.ArrayInterfaceStr()});
|
||||
auto const& h_result = info.base_margin_.View(-1);
|
||||
ASSERT_EQ(h_result.Shape().size(), 3);
|
||||
auto in_margin = base_margin.View(-1);
|
||||
linalg::ElementWiseKernelHost(h_result, omp_get_max_threads(), [&](size_t i, float v_0) {
|
||||
auto tup = linalg::UnravelIndex(i, h_result.Shape());
|
||||
auto i0 = std::get<0>(tup);
|
||||
auto i1 = std::get<1>(tup);
|
||||
auto i2 = std::get<2>(tup);
|
||||
// Sliced at 3^th dimension.
|
||||
auto v_1 = in_margin(i0, i1, 0, i2);
|
||||
CHECK_EQ(v_0, v_1);
|
||||
return v_0;
|
||||
});
|
||||
}
|
||||
}
|
||||
} // namespace xgboost
|
||||
#endif // XGBOOST_TESTS_CPP_DATA_TEST_METAINFO_H_
|
||||
@ -253,8 +253,8 @@ TEST(SimpleDMatrix, Slice) {
|
||||
std::iota(lower.begin(), lower.end(), 0.0f);
|
||||
std::iota(upper.begin(), upper.end(), 1.0f);
|
||||
|
||||
auto& margin = p_m->Info().base_margin_.HostVector();
|
||||
margin.resize(kRows * kClasses);
|
||||
auto& margin = p_m->Info().base_margin_;
|
||||
margin = linalg::Tensor<float, 3>{{kRows, kClasses}, GenericParameter::kCpuId};
|
||||
|
||||
std::array<int32_t, 3> ridxs {1, 3, 5};
|
||||
std::unique_ptr<DMatrix> out { p_m->Slice(ridxs) };
|
||||
@ -284,10 +284,10 @@ TEST(SimpleDMatrix, Slice) {
|
||||
ASSERT_EQ(p_m->Info().weights_.HostVector().at(ridx),
|
||||
out->Info().weights_.HostVector().at(i));
|
||||
|
||||
auto& out_margin = out->Info().base_margin_.HostVector();
|
||||
auto out_margin = out->Info().base_margin_.View(GenericParameter::kCpuId);
|
||||
auto in_margin = margin.View(GenericParameter::kCpuId);
|
||||
for (size_t j = 0; j < kClasses; ++j) {
|
||||
auto in_beg = ridx * kClasses;
|
||||
ASSERT_EQ(out_margin.at(i * kClasses + j), margin.at(in_beg + j));
|
||||
ASSERT_EQ(out_margin(i, j), in_margin(ridx, j));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -122,13 +122,13 @@ TEST(SimpleDMatrix, FromColumnarWithEmptyRows) {
|
||||
col["data"] = j_data;
|
||||
std::vector<Json> j_shape{Json(Integer(static_cast<Integer::Int>(kRows)))};
|
||||
col["shape"] = Array(j_shape);
|
||||
col["version"] = Integer(static_cast<Integer::Int>(1));
|
||||
col["version"] = 3;
|
||||
col["typestr"] = String("<f4");
|
||||
|
||||
// Construct the mask object.
|
||||
col["mask"] = Object();
|
||||
auto& j_mask = col["mask"];
|
||||
j_mask["version"] = Integer(static_cast<Integer::Int>(1));
|
||||
j_mask["version"] = 3;
|
||||
auto& mask_storage = column_bitfields[i];
|
||||
mask_storage.resize(16); // 16 bytes
|
||||
|
||||
@ -220,7 +220,7 @@ TEST(SimpleCSRSource, FromColumnarSparse) {
|
||||
for (size_t c = 0; c < kCols; ++c) {
|
||||
auto& column = j_columns[c];
|
||||
column = Object();
|
||||
column["version"] = Integer(static_cast<Integer::Int>(1));
|
||||
column["version"] = 3;
|
||||
column["typestr"] = String("<f4");
|
||||
auto p_d_data = raw_pointer_cast(columns_data[c].data());
|
||||
std::vector<Json> j_data {
|
||||
@ -229,12 +229,12 @@ TEST(SimpleCSRSource, FromColumnarSparse) {
|
||||
column["data"] = j_data;
|
||||
std::vector<Json> j_shape {Json(Integer(static_cast<Integer::Int>(kRows)))};
|
||||
column["shape"] = Array(j_shape);
|
||||
column["version"] = Integer(static_cast<Integer::Int>(1));
|
||||
column["version"] = 3;
|
||||
column["typestr"] = String("<f4");
|
||||
|
||||
column["mask"] = Object();
|
||||
auto& j_mask = column["mask"];
|
||||
j_mask["version"] = Integer(static_cast<Integer::Int>(1));
|
||||
j_mask["version"] = 3;
|
||||
j_mask["data"] = std::vector<Json>{
|
||||
Json(Integer(reinterpret_cast<Integer::Int>(column_bitfields[c].data().get()))),
|
||||
Json(Boolean(false))};
|
||||
|
||||
@ -228,6 +228,7 @@ RandomDataGenerator::GenerateArrayInterfaceBatch(
|
||||
if (device_ >= 0) {
|
||||
array_interface["data"][0] =
|
||||
Integer(reinterpret_cast<int64_t>(storage->DevicePointer() + offset));
|
||||
array_interface["stream"] = Null{};
|
||||
} else {
|
||||
array_interface["data"][0] =
|
||||
Integer(reinterpret_cast<int64_t>(storage->HostPointer() + offset));
|
||||
@ -240,7 +241,7 @@ RandomDataGenerator::GenerateArrayInterfaceBatch(
|
||||
array_interface["shape"][1] = cols_;
|
||||
|
||||
array_interface["typestr"] = String("<f4");
|
||||
array_interface["version"] = 1;
|
||||
array_interface["version"] = 3;
|
||||
return array_interface;
|
||||
};
|
||||
|
||||
|
||||
@ -188,6 +188,7 @@ Json GetArrayInterface(HostDeviceVector<T> *storage, size_t rows, size_t cols) {
|
||||
if (storage->DeviceCanRead()) {
|
||||
array_interface["data"][0] =
|
||||
Integer(reinterpret_cast<int64_t>(storage->ConstDevicePointer()));
|
||||
array_interface["stream"] = nullptr;
|
||||
} else {
|
||||
array_interface["data"][0] =
|
||||
Integer(reinterpret_cast<int64_t>(storage->ConstHostPointer()));
|
||||
@ -200,7 +201,7 @@ Json GetArrayInterface(HostDeviceVector<T> *storage, size_t rows, size_t cols) {
|
||||
|
||||
char t = linalg::detail::ArrayInterfaceHandler::TypeChar<T>();
|
||||
array_interface["typestr"] = String(std::string{"<"} + t + std::to_string(sizeof(T)));
|
||||
array_interface["version"] = 1;
|
||||
array_interface["version"] = 3;
|
||||
return array_interface;
|
||||
}
|
||||
|
||||
|
||||
@ -108,7 +108,9 @@ TEST(GPUPredictor, ExternalMemoryTest) {
|
||||
dmats.push_back(CreateSparsePageDMatrix(8000));
|
||||
|
||||
for (const auto& dmat: dmats) {
|
||||
dmat->Info().base_margin_.Resize(dmat->Info().num_row_ * n_classes, 0.5);
|
||||
dmat->Info().base_margin_ =
|
||||
linalg::Tensor<float, 3>{{dmat->Info().num_row_, static_cast<size_t>(n_classes)}, 0};
|
||||
dmat->Info().base_margin_.Data()->Fill(0.5);
|
||||
PredictionCacheEntry out_predictions;
|
||||
gpu_predictor->InitOutPredictions(dmat->Info(), &out_predictions.predictions, model);
|
||||
gpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
|
||||
|
||||
@ -17,7 +17,7 @@ rng = np.random.RandomState(1994)
|
||||
|
||||
def set_base_margin_info(DType, DMatrixT, tm: str):
|
||||
rng = np.random.default_rng()
|
||||
X = DType(rng.normal(0, 1.0, size=100).reshape(50, 2))
|
||||
X = DType(rng.normal(0, 1.0, size=100).astype(np.float32).reshape(50, 2))
|
||||
if hasattr(X, "iloc"):
|
||||
y = X.iloc[:, 0]
|
||||
else:
|
||||
@ -29,8 +29,35 @@ def set_base_margin_info(DType, DMatrixT, tm: str):
|
||||
with pytest.raises(ValueError, match=r".*base_margin.*"):
|
||||
xgb.train({"tree_method": tm}, Xy)
|
||||
|
||||
# FIXME(jiamingy): Currently the metainfo has no concept of shape. If you pass a
|
||||
# base_margin with shape (n_classes, n_samples) to XGBoost the result is undefined.
|
||||
if not hasattr(X, "iloc"):
|
||||
# column major matrix
|
||||
got = DType(Xy.get_base_margin().reshape(50, 2))
|
||||
assert (got == base_margin).all()
|
||||
|
||||
assert base_margin.T.flags.c_contiguous is False
|
||||
assert base_margin.T.flags.f_contiguous is True
|
||||
Xy.set_info(base_margin=base_margin.T)
|
||||
got = DType(Xy.get_base_margin().reshape(2, 50))
|
||||
assert (got == base_margin.T).all()
|
||||
|
||||
# Row vs col vec.
|
||||
base_margin = y
|
||||
Xy.set_base_margin(base_margin)
|
||||
bm_col = Xy.get_base_margin()
|
||||
Xy.set_base_margin(base_margin.reshape(1, base_margin.size))
|
||||
bm_row = Xy.get_base_margin()
|
||||
assert (bm_row == bm_col).all()
|
||||
|
||||
# type
|
||||
base_margin = base_margin.astype(np.float64)
|
||||
Xy.set_base_margin(base_margin)
|
||||
bm_f64 = Xy.get_base_margin()
|
||||
assert (bm_f64 == bm_col).all()
|
||||
|
||||
# too many dimensions
|
||||
base_margin = X.reshape(2, 5, 2, 5)
|
||||
with pytest.raises(ValueError, match=r".*base_margin.*"):
|
||||
Xy.set_base_margin(base_margin)
|
||||
|
||||
|
||||
class TestDMatrix:
|
||||
@ -141,6 +168,7 @@ class TestDMatrix:
|
||||
# base margin is per-class in multi-class classifier
|
||||
base_margin = rng.randn(100, 3).astype(np.float32)
|
||||
d.set_base_margin(base_margin)
|
||||
np.testing.assert_allclose(d.get_base_margin().reshape(100, 3), base_margin)
|
||||
|
||||
ridxs = [1, 2, 3, 4, 5, 6]
|
||||
sliced = d.slice(ridxs)
|
||||
@ -154,7 +182,7 @@ class TestDMatrix:
|
||||
# Slicing a DMatrix results into a DMatrix that's equivalent to a DMatrix that's
|
||||
# constructed from the corresponding NumPy slice
|
||||
d2 = xgb.DMatrix(X[1:7, :], y[1:7])
|
||||
d2.set_base_margin(base_margin[1:7, :].flatten())
|
||||
d2.set_base_margin(base_margin[1:7, :])
|
||||
eval_res = {}
|
||||
_ = xgb.train(
|
||||
{'num_class': 3, 'objective': 'multi:softprob',
|
||||
@ -280,7 +308,7 @@ class TestDMatrix:
|
||||
m.set_info(feature_weights=fw)
|
||||
np.testing.assert_allclose(fw, m.get_float_info('feature_weights'))
|
||||
# Handle empty
|
||||
m.set_info(feature_weights=np.empty((0, 0)))
|
||||
m.set_info(feature_weights=np.empty((0, )))
|
||||
|
||||
assert m.get_float_info('feature_weights').shape[0] == 0
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user