|
| int | TreeliteLoadXGBoostModelLegacyBinary (char const *filename, char const *config_json, TreeliteModelHandle *out) |
| | Load a model file generated by XGBoost (dmlc/xgboost), stored in the legacy binary format. More...
|
| |
| int | TreeliteLoadXGBoostModelLegacyBinaryFromMemoryBuffer (void const *buf, size_t len, char const *config_json, TreeliteModelHandle *out) |
| | Load an XGBoost model from a memory buffer using the legacy binary format. More...
|
| |
| int | TreeliteLoadXGBoostModel (char const *filename, char const *config_json, TreeliteModelHandle *out) |
| | Load a model file generated by XGBoost (dmlc/xgboost), stored in the JSON format. More...
|
| |
| int | TreeliteLoadXGBoostModelFromString (char const *json_str, size_t length, char const *config_json, TreeliteModelHandle *out) |
| | Load an XGBoost model from a JSON string. More...
|
| |
| int | TreeliteLoadLightGBMModel (char const *filename, char const *config_json, TreeliteModelHandle *out) |
| | Load a model file generated by LightGBM (Microsoft/LightGBM). The model file must contain a decision tree ensemble. More...
|
| |
| int | TreeliteLoadLightGBMModelFromString (char const *model_str, char const *config_json, TreeliteModelHandle *out) |
| | Load a LightGBM model from a string. The string should be created with the model_to_string() method in LightGBM. More...
|
| |
| int | TreeliteLoadSKLearnRandomForestRegressor (int n_estimators, int n_features, int n_targets, int64_t const *node_count, int64_t const **children_left, int64_t const **children_right, int64_t const **feature, double const **threshold, double const **value, int64_t const **n_node_samples, double const **weighted_n_node_samples, double const **impurity, TreeliteModelHandle *out) |
| | Load a scikit-learn RandomForestRegressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note that this function can also be used to load an ensemble of extremely randomized trees (sklearn.ensemble.ExtraTreesRegressor). More...
|
| |
| int | TreeliteLoadSKLearnIsolationForest (int n_estimators, int n_features, int64_t const *node_count, int64_t const **children_left, int64_t const **children_right, int64_t const **feature, double const **threshold, double const **value, int64_t const **n_node_samples, double const **weighted_n_node_samples, double const **impurity, double ratio_c, TreeliteModelHandle *out) |
| | Load a scikit-learn IsolationForest model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. More...
|
| |
| int | TreeliteLoadSKLearnRandomForestClassifier (int n_estimators, int n_features, int n_targets, int32_t const *n_classes, int64_t const *node_count, int64_t const **children_left, int64_t const **children_right, int64_t const **feature, double const **threshold, double const **value, int64_t const **n_node_samples, double const **weighted_n_node_samples, double const **impurity, TreeliteModelHandle *out) |
| | Load a scikit-learn RandomForestClassifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note that this function can also be used to load an ensemble of extremely randomized trees (sklearn.ensemble.ExtraTreesClassifier). More...
|
| |
| int | TreeliteLoadSKLearnGradientBoostingRegressor (int n_iter, int n_features, int64_t const *node_count, int64_t const **children_left, int64_t const **children_right, int64_t const **feature, double const **threshold, double const **value, int64_t const **n_node_samples, double const **weighted_n_node_samples, double const **impurity, double const *base_scores, TreeliteModelHandle *out) |
| | Load a scikit-learn GradientBoostingRegressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note: GradientBoostingRegressor does not support multiple targets (outputs). More...
|
| |
| int | TreeliteLoadSKLearnGradientBoostingClassifier (int n_iter, int n_features, int n_classes, int64_t const *node_count, int64_t const **children_left, int64_t const **children_right, int64_t const **feature, double const **threshold, double const **value, int64_t const **n_node_samples, double const **weighted_n_node_samples, double const **impurity, double const *base_scores, TreeliteModelHandle *out) |
| | Load a scikit-learn GradientBoostingClassifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the meaning of the arrays in detail. Note: GradientBoostingClassifier does not support multiple targets (outputs). More...
|
| |
| int | TreeliteLoadSKLearnHistGradientBoostingRegressor (int n_iter, int n_features, int64_t const *node_count, void const **nodes, int expected_sizeof_node_struct, uint32_t n_categorical_splits, uint32_t const **raw_left_cat_bitsets, uint32_t const *known_cat_bitsets, uint32_t const *known_cat_bitsets_offset_map, int32_t const *features_map, int64_t const **categories_map, double const *base_scores, TreeliteModelHandle *out) |
| | Load a scikit-learn HistGradientBoostingRegressor model from a collection of arrays. Note: HistGradientBoostingRegressor does not support multiple targets (outputs). More...
|
| |
| int | TreeliteLoadSKLearnHistGradientBoostingClassifier (int n_iter, int n_features, int n_classes, int64_t const *node_count, void const **nodes, int expected_sizeof_node_struct, uint32_t n_categorical_splits, uint32_t const **raw_left_cat_bitsets, uint32_t const *known_cat_bitsets, uint32_t const *known_cat_bitsets_offset_map, int32_t const *features_map, int64_t const **categories_map, double const *base_scores, TreeliteModelHandle *out) |
| | Load a scikit-learn HistGradientBoostingClassifier model from a collection of arrays. Note: HistGradientBoostingClassifier does not support multiple targets (outputs). More...
|
| |
| int | TreeliteGetModelBuilder (char const *json_str, TreeliteModelBuilderHandle *out) |
| | Initialize a model builder object from a JSON string. More...
|
| |
| int | TreeliteDeleteModelBuilder (TreeliteModelBuilderHandle model_builder) |
| | Delete model builder object from memory. More...
|
| |
| int | TreeliteModelBuilderStartTree (TreeliteModelBuilderHandle model_builder) |
| | Start a new tree. More...
|
| |
| int | TreeliteModelBuilderEndTree (TreeliteModelBuilderHandle model_builder) |
| | End the current tree. More...
|
| |
| int | TreeliteModelBuilderStartNode (TreeliteModelBuilderHandle model_builder, int node_key) |
| | Start a new node. More...
|
| |
| int | TreeliteModelBuilderEndNode (TreeliteModelBuilderHandle model_builder) |
| | End the current node. More...
|
| |
| int | TreeliteModelBuilderNumericalTest (TreeliteModelBuilderHandle model_builder, int32_t split_index, double threshold, int default_left, char const *cmp, int left_child_key, int right_child_key) |
| | Declare the current node as a numerical test node, where the test is of form [feature value] [cmp] [threshold]. Data points for which the test evaluates to True will be mapped to the left child node; all other data points (for which the test evaluates to False) will be mapped to the right child node. More...
|
| |
| int | TreeliteModelBuilderCategoricalTest (TreeliteModelBuilderHandle model_builder, int32_t split_index, int default_left, uint32_t const *category_list, size_t category_list_len, int category_list_right_child, int left_child_key, int right_child_key) |
| | Declare the current node as a categorical test node, where the test is of form [feature value] \in [category list]. More...
|
| |
| int | TreeliteModelBuilderLeafScalar (TreeliteModelBuilderHandle model_builder, double leaf_value) |
| | Declare the current node as a leaf node with a scalar output. More...
|
| |
| int | TreeliteModelBuilderLeafVectorFloat32 (TreeliteModelBuilderHandle model_builder, float const *leaf_vector, size_t leaf_vector_len) |
| | Declare the current node as a leaf node with a vector output (float32) More...
|
| |
| int | TreeliteModelBuilderLeafVectorFloat64 (TreeliteModelBuilderHandle model_builder, double const *leaf_vector, size_t leaf_vector_len) |
| | Declare the current node as a leaf node with a vector output (float64) More...
|
| |
| int | TreeliteModelBuilderGain (TreeliteModelBuilderHandle model_builder, double gain) |
| | Specify the gain (loss reduction) that's resulted from the current split. More...
|
| |
| int | TreeliteModelBuilderDataCount (TreeliteModelBuilderHandle model_builder, uint64_t data_count) |
| | Specify the number of data points (samples) that are mapped to the current node. More...
|
| |
| int | TreeliteModelBuilderSumHess (TreeliteModelBuilderHandle model_builder, double sum_hess) |
| | Specify the weighted sample count or the sum of Hessians for the data points that are mapped to the current node. More...
|
| |
| int | TreeliteModelBuilderCommitModel (TreeliteModelBuilderHandle model_builder, TreeliteModelHandle *out) |
| | Conclude model building and obtain the final model object. More...
|
| |
| int | TreeliteDumpAsJSON (TreeliteModelHandle handle, int pretty_print, char const **out_json_str) |
| | Dump a model object as a JSON string. More...
|
| |
| int | TreeliteGetInputType (TreeliteModelHandle model, char const **out_str) |
| | Query the input type of a Treelite model object. More...
|
| |
| int | TreeliteGetOutputType (TreeliteModelHandle model, char const **out_str) |
| | Query the output type of a Treelite model object. More...
|
| |
| int | TreeliteQueryNumTree (TreeliteModelHandle model, size_t *out) |
| | Query the number of trees in the model. More...
|
| |
| int | TreeliteQueryNumFeature (TreeliteModelHandle model, int *out) |
| | Query the number of features used in the model. More...
|
| |
| int | TreeliteConcatenateModelObjects (TreeliteModelHandle const *objs, size_t len, TreeliteModelHandle *out) |
| | Concatenate multiple model objects into a single model object by copying all member trees into the destination model object. More...
|
| |
| int | TreeliteFreeModel (TreeliteModelHandle handle) |
| | Delete model from memory. More...
|
| |
| int | TreeliteSerializeModelToFile (TreeliteModelHandle handle, char const *filename) |
| | Serialize (persist) a model object to disk. More...
|
| |
| int | TreeliteDeserializeModelFromFile (char const *filename, TreeliteModelHandle *out) |
| | Deserialize (load) a model object from disk. More...
|
| |
| int | TreeliteSerializeModelToBytes (TreeliteModelHandle handle, char const **out_bytes, size_t *out_bytes_len) |
| | Serialize (persist) a model object to a byte sequence. More...
|
| |
| int | TreeliteDeserializeModelFromBytes (char const *bytes, size_t bytes_len, TreeliteModelHandle *out) |
| | Deserialize (load) a model object from a byte sequence. More...
|
| |
| int | TreeliteSerializeModelToPyBuffer (TreeliteModelHandle handle, TreelitePyBufferFrame **out_frames, size_t *out_num_frames) |
| | Serialize a model object using the Python buffer protocol (PEP 3118). More...
|
| |
| int | TreeliteDeserializeModelFromPyBuffer (TreelitePyBufferFrame *frames, size_t num_frames, TreeliteModelHandle *out) |
| | Deserialize a model object using the Python buffer protocol (PEP 3118). More...
|
| |
| int | TreeliteGTILParseConfig (char const *config_json, TreeliteGTILConfigHandle *out) |
| | Load a configuration for GTIL predictor from a JSON string. More...
|
| |
| int | TreeliteGTILDeleteConfig (TreeliteGTILConfigHandle handle) |
| | Delete a GTIL configuration from memory. More...
|
| |
| int | TreeliteGTILGetOutputShape (TreeliteModelHandle model, uint64_t num_row, TreeliteGTILConfigHandle config, uint64_t const **out, uint64_t *out_ndim) |
| | Given a data matrix, query the necessary shape of array to hold predictions for all data points. More...
|
| |
| int | TreeliteGTILPredict (TreeliteModelHandle model, void const *input, char const *input_type, uint64_t num_row, void *output, TreeliteGTILConfigHandle config) |
| | Predict with a 2D dense array. More...
|
| |
| int | TreeliteGTILPredictSparse (TreeliteModelHandle model, void const *data, char const *input_type, uint64_t const *col_ind, uint64_t const *row_ptr, uint64_t num_row, void *output, TreeliteGTILConfigHandle config) |
| | Predict with sparse data with CSR (compressed sparse row) layout. More...
|
| |
| int | TreeliteGetHeaderField (TreeliteModelHandle model, char const *name, TreelitePyBufferFrame *out_frame) |
| | Get a field in the header. More...
|
| |
| int | TreeliteGetTreeField (TreeliteModelHandle model, uint64_t tree_id, char const *name, TreelitePyBufferFrame *out_frame) |
| | Get a field in a tree. More...
|
| |
| int | TreeliteSetHeaderField (TreeliteModelHandle model, char const *name, TreelitePyBufferFrame frame) |
| | Set a field in the header. More...
|
| |
| int | TreeliteSetTreeField (TreeliteModelHandle model, uint64_t tree_id, char const *name, TreelitePyBufferFrame frame) |
| | Set a field in a tree. More...
|
| |
| char const * | TreeliteGetLastError (void) |
| | Display last error; can be called by multiple threads Note. Each thread will get the last error occured in its own context. More...
|
| |
| int | TreeliteRegisterLogCallback (void(*callback)(char const *)) |
| | Register callback function for LOG(INFO) messages – helpful messages that are not errors. Note: This function can be called by multiple threads. The callback function will run on the thread that registered it. More...
|
| |
| int | TreeliteRegisterWarningCallback (void(*callback)(char const *)) |
| | Register callback function for LOG(WARNING) messages Note: This function can be called by multiple threads. The callback function will run on the thread that registered it. More...
|
| |
