Treelite
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Functions | |
int | TreeliteLoadLightGBMModel (const char *filename, ModelHandle *out) |
load a model file generated by LightGBM (Microsoft/LightGBM). The model file must contain a decision tree ensemble. More... | |
int | TreeliteLoadXGBoostModel (const char *filename, ModelHandle *out) |
load a model file generated by XGBoost (dmlc/xgboost). The model file must contain a decision tree ensemble. More... | |
int | TreeliteLoadXGBoostJSON (const char *filename, ModelHandle *out) |
load a json model file generated by XGBoost (dmlc/xgboost). The model file must contain a decision tree ensemble. More... | |
int | TreeliteLoadXGBoostJSONString (const char *json_str, size_t length, ModelHandle *out) |
load a model stored as JSON stringby XGBoost (dmlc/xgboost). The model json must contain a decision tree ensemble. More... | |
int | TreeliteLoadXGBoostModelFromMemoryBuffer (const void *buf, size_t len, ModelHandle *out) |
load an XGBoost model from a memory buffer. More... | |
int | TreeliteLoadLightGBMModelFromString (const char *model_str, ModelHandle *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, const int64_t *node_count, const int64_t **children_left, const int64_t **children_right, const int64_t **feature, const double **threshold, const double **value, const int64_t **n_node_samples, const double **weighted_n_node_samples, const double **impurity, ModelHandle *out) |
Load a scikit-learn random forest regressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning 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, const int64_t *node_count, const int64_t **children_left, const int64_t **children_right, const int64_t **feature, const double **threshold, const double **value, const int64_t **n_node_samples, const double **weighted_n_node_samples, const double **impurity, const double ratio_c, ModelHandle *out) |
Load a scikit-learn isolation forest model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning of the arrays in detail. More... | |
int | TreeliteLoadSKLearnRandomForestClassifier (int n_estimators, int n_features, int n_classes, const int64_t *node_count, const int64_t **children_left, const int64_t **children_right, const int64_t **feature, const double **threshold, const double **value, const int64_t **n_node_samples, const double **weighted_n_node_samples, const double **impurity, ModelHandle *out) |
Load a scikit-learn random forest classifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning 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_estimators, int n_features, const int64_t *node_count, const int64_t **children_left, const int64_t **children_right, const int64_t **feature, const double **threshold, const double **value, const int64_t **n_node_samples, const double **weighted_n_node_samples, const double **impurity, ModelHandle *out) |
Load a scikit-learn gradient boosting regressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning of the arrays in detail. More... | |
int | TreeliteLoadSKLearnGradientBoostingClassifier (int n_estimators, int n_features, int n_classes, const int64_t *node_count, const int64_t **children_left, const int64_t **children_right, const int64_t **feature, const double **threshold, const double **value, const int64_t **n_node_samples, const double **weighted_n_node_samples, const double **impurity, ModelHandle *out) |
Load a scikit-learn gradient boosting classifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning of the arrays in detail. More... | |
int | TreeliteQueryNumTree (ModelHandle handle, size_t *out) |
Query the number of trees in the model. More... | |
int | TreeliteQueryNumFeature (ModelHandle handle, size_t *out) |
Query the number of features used in the model. More... | |
int | TreeliteQueryNumClass (ModelHandle handle, size_t *out) |
Query the number of classes of the model. (1 if the model is binary classifier or regressor) More... | |
int | TreeliteSetTreeLimit (ModelHandle handle, size_t limit) |
keep first N trees of model, limit must smaller than number of trees. More... | |
int | TreeliteSerializeModel (const char *filename, ModelHandle handle) |
Serialize (persist) a model object to disk. More... | |
int | TreeliteDeserializeModel (const char *filename, ModelHandle *out) |
Deserialize (load) a model object from disk. More... | |
int | TreeliteDumpAsJSON (ModelHandle handle, int pretty_print, const char **out_json_str) |
Dump a model object as a JSON string. More... | |
int | TreeliteFreeModel (ModelHandle handle) |
delete model from memory More... | |
Model loader interface: read trees from the disk
int TreeliteDeserializeModel | ( | const char * | filename, |
ModelHandle * | out | ||
) |
Deserialize (load) a model object from disk.
filename | name of the file from which to deserialize the model. The file should be created by a call to TreeliteSerializeModel(). |
handle | handle to the model object |
int TreeliteDumpAsJSON | ( | ModelHandle | handle, |
int | pretty_print, | ||
const char ** | out_json_str | ||
) |
int TreeliteFreeModel | ( | ModelHandle | handle | ) |
int TreeliteLoadLightGBMModel | ( | const char * | filename, |
ModelHandle * | out | ||
) |
int TreeliteLoadLightGBMModelFromString | ( | const char * | model_str, |
ModelHandle * | out | ||
) |
int TreeliteLoadSKLearnGradientBoostingClassifier | ( | int | n_estimators, |
int | n_features, | ||
int | n_classes, | ||
const int64_t * | node_count, | ||
const int64_t ** | children_left, | ||
const int64_t ** | children_right, | ||
const int64_t ** | feature, | ||
const double ** | threshold, | ||
const double ** | value, | ||
const int64_t ** | n_node_samples, | ||
const double ** | weighted_n_node_samples, | ||
const double ** | impurity, | ||
ModelHandle * | out | ||
) |
Load a scikit-learn gradient boosting classifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning of the arrays in detail.
n_estimators | number of trees in the random forest |
n_features | number of features in the training data |
n_classes | number of classes in the target variable |
node_count | node_count[i] stores the number of nodes in the i-th tree |
children_left | children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
children_right | children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
feature | feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
threshold | threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
value | value[i][k] stores the leaf output of node k of the i-th tree. This is only defined if node k is a leaf node. |
n_node_samples | n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree. |
weighted_n_node_samples | weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree. |
impurity | impurity[i][k] stores the impurity measure (gini, entropy etc) associated with node k of the i-th tree. |
out | pointer to store the loaded model |
int TreeliteLoadSKLearnGradientBoostingRegressor | ( | int | n_estimators, |
int | n_features, | ||
const int64_t * | node_count, | ||
const int64_t ** | children_left, | ||
const int64_t ** | children_right, | ||
const int64_t ** | feature, | ||
const double ** | threshold, | ||
const double ** | value, | ||
const int64_t ** | n_node_samples, | ||
const double ** | weighted_n_node_samples, | ||
const double ** | impurity, | ||
ModelHandle * | out | ||
) |
Load a scikit-learn gradient boosting regressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning of the arrays in detail.
n_estimators | number of trees in the random forest |
n_features | number of features in the training data |
node_count | node_count[i] stores the number of nodes in the i-th tree |
children_left | children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
children_right | children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
feature | feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
threshold | threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
value | value[i][k] stores the leaf output of node k of the i-th tree. This is only defined if node k is a leaf node. |
n_node_samples | n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree. |
weighted_n_node_samples | weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree. |
impurity | impurity[i][k] stores the impurity measure (gini, entropy etc) associated with node k of the i-th tree. |
out | pointer to store the loaded model |
int TreeliteLoadSKLearnIsolationForest | ( | int | n_estimators, |
int | n_features, | ||
const int64_t * | node_count, | ||
const int64_t ** | children_left, | ||
const int64_t ** | children_right, | ||
const int64_t ** | feature, | ||
const double ** | threshold, | ||
const double ** | value, | ||
const int64_t ** | n_node_samples, | ||
const double ** | weighted_n_node_samples, | ||
const double ** | impurity, | ||
const double | ratio_c, | ||
ModelHandle * | out | ||
) |
Load a scikit-learn isolation forest model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning of the arrays in detail.
n_estimators | number of trees in the random forest |
n_features | number of features in the training data |
node_count | node_count[i] stores the number of nodes in the i-th tree |
children_left | children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
children_right | children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
feature | feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
threshold | threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
value | value[i][k] stores the expected isolation depth of node k of the i-th tree. This is only defined if node k is a leaf node. |
n_node_samples | n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree. |
weighted_n_node_samples | weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree. |
impurity | not used, but must be passed as array of arrays for each tree and node. |
ratio_c | standardizing constant to use for calculation of the anomaly score. |
out | pointer to store the loaded model |
int TreeliteLoadSKLearnRandomForestClassifier | ( | int | n_estimators, |
int | n_features, | ||
int | n_classes, | ||
const int64_t * | node_count, | ||
const int64_t ** | children_left, | ||
const int64_t ** | children_right, | ||
const int64_t ** | feature, | ||
const double ** | threshold, | ||
const double ** | value, | ||
const int64_t ** | n_node_samples, | ||
const double ** | weighted_n_node_samples, | ||
const double ** | impurity, | ||
ModelHandle * | out | ||
) |
Load a scikit-learn random forest classifier model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning of the arrays in detail. Note that this function can also be used to load an ensemble of extremely randomized trees (sklearn.ensemble.ExtraTreesClassifier).
n_estimators | number of trees in the random forest |
n_features | number of features in the training data |
n_classes | number of classes in the target variable |
node_count | node_count[i] stores the number of nodes in the i-th tree |
children_left | children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
children_right | children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
feature | feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
threshold | threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
value | value[i][k] stores the leaf output of node k of the i-th tree. This is only defined if node k is a leaf node. |
n_node_samples | n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree. |
weighted_n_node_samples | weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree. |
impurity | impurity[i][k] stores the impurity measure (gini, entropy etc) associated with node k of the i-th tree. |
out | pointer to store the loaded model |
int TreeliteLoadSKLearnRandomForestRegressor | ( | int | n_estimators, |
int | n_features, | ||
const int64_t * | node_count, | ||
const int64_t ** | children_left, | ||
const int64_t ** | children_right, | ||
const int64_t ** | feature, | ||
const double ** | threshold, | ||
const double ** | value, | ||
const int64_t ** | n_node_samples, | ||
const double ** | weighted_n_node_samples, | ||
const double ** | impurity, | ||
ModelHandle * | out | ||
) |
Load a scikit-learn random forest regressor model from a collection of arrays. Refer to https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html to learn the mearning of the arrays in detail. Note that this function can also be used to load an ensemble of extremely randomized trees (sklearn.ensemble.ExtraTreesRegressor).
n_estimators | number of trees in the random forest |
n_features | number of features in the training data |
node_count | node_count[i] stores the number of nodes in the i-th tree |
children_left | children_left[i][k] stores the ID of the left child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
children_right | children_right[i][k] stores the ID of the right child node of node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
feature | feature[i][k] stores the ID of the feature used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
threshold | threshold[i][k] stores the threshold used in the binary tree split at node k of the i-th tree. This is only defined if node k is an internal (non-leaf) node. |
value | value[i][k] stores the leaf output of node k of the i-th tree. This is only defined if node k is a leaf node. |
n_node_samples | n_node_samples[i][k] stores the number of data samples associated with node k of the i-th tree. |
weighted_n_node_samples | weighted_n_node_samples[i][k] stores the sum of weighted data samples associated with node k of the i-th tree. |
impurity | impurity[i][k] stores the impurity measure (gini, entropy etc) associated with node k of the i-th tree. |
out | pointer to store the loaded model |
int TreeliteLoadXGBoostJSON | ( | const char * | filename, |
ModelHandle * | out | ||
) |
int TreeliteLoadXGBoostJSONString | ( | const char * | json_str, |
size_t | length, | ||
ModelHandle * | out | ||
) |
load a model stored as JSON stringby XGBoost (dmlc/xgboost). The model json must contain a decision tree ensemble.
json_str | the string containing the JSON model specification |
length | the length of the JSON string |
out | loaded model |
int TreeliteLoadXGBoostModel | ( | const char * | filename, |
ModelHandle * | out | ||
) |
int TreeliteLoadXGBoostModelFromMemoryBuffer | ( | const void * | buf, |
size_t | len, | ||
ModelHandle * | out | ||
) |
int TreeliteQueryNumClass | ( | ModelHandle | handle, |
size_t * | out | ||
) |
int TreeliteQueryNumFeature | ( | ModelHandle | handle, |
size_t * | out | ||
) |
int TreeliteQueryNumTree | ( | ModelHandle | handle, |
size_t * | out | ||
) |
int TreeliteSerializeModel | ( | const char * | filename, |
ModelHandle | handle | ||
) |
Serialize (persist) a model object to disk.
filename | name of the file to which to serialize the model. The file will be using a binary format that's optimized to store the Treelite model object efficiently. |
handle | handle to the model object |
int TreeliteSetTreeLimit | ( | ModelHandle | handle, |
size_t | limit | ||
) |