Treelite API¶

API of Treelite Python package.

Treelite: a model compiler for decision tree ensembles

class treelite.Model(handle=None)¶

Decision tree ensemble model

Parameters: handle (ctypes.c_void_p, optional) – Initial value of model handle

compile(dirpath, params=None, compiler='ast_native', verbose=False)¶

Generate prediction code from a tree ensemble model. The code will be C99 compliant. One header file (.h) will be generated, along with one or more source files (.c). Use create_shared() method to package prediction code as a dynamic shared library (.so/.dll/.dylib).

Parameters

dirpath (str) – directory to store header and source files
params (dict, optional) – parameters for compiler. See this page for the list of compiler parameters.
compiler (str, optional) – name of compiler to use
verbose (bool, optional) – Whether to print extra messages during compilation

Example

The following populates the directory ./model with source and header files:

model.compile(dirpath='./my/model', params={}, verbose=True)

If parallel compilation is enabled (parameter parallel_comp), the files are in the form of ./my/model/header.h, ./my/model/main.c, ./my/model/tu0.c, ./my/model/tu1.c and so forth, depending on the value of parallel_comp. Otherwise, there will be exactly two files: ./model/header.h, ./my/model/main.c

classmethod deserialize(filename)¶

Deserialize (recover) the model from a checkpoint file in the disk. It is expected that the file was generated by a call to the serialize() method.

Note

Use exactly matching versions of Treelite when exchanging checkpoints

We provide ZERO backward compatibility guarantee. You will not be able to recover the model from a checkpoint that was generated by a previous version of Treelite. Both the producer and the consumer of the checkpoint must have the identical major and minor versions of Treelite.

Parameters: filename (str) – Path to checkpoint
Returns: model – Recovered model
Return type: Model object

export_lib(toolchain, libpath, params=None, compiler='ast_native', verbose=False, nthread=None, options=None)¶

Convenience function: Generate prediction code and immediately turn it into a dynamic shared library. A temporary directory will be created to hold the source files.

Parameters

toolchain (str) – which toolchain to use. You may choose one of ‘msvc’, ‘clang’, and ‘gcc’. You may also specify a specific variation of clang or gcc (e.g. ‘gcc-7’)
libpath (str) – location to save the generated dynamic shared library
params (dict, optional) – parameters to be passed to the compiler. See this page for the list of compiler parameters.
compiler (str, optional) – name of compiler to use in C code generation
verbose (bool, optional) – whether to produce extra messages
nthread (int, optional) – number of threads to use in creating the shared library. Defaults to the number of cores in the system.
options (list of str, optional) – Additional options to pass to toolchain

Example

The one-line command

model.export_lib(toolchain='msvc', libpath='./mymodel.dll',
                 params={}, verbose=True)

is equivalent to the following sequence of commands:

model.compile(dirpath='/temporary/directory', params={}, verbose=True)
treelite.create_shared(toolchain='msvc', dirpath='/temporary/directory',
              verbose=True)
# move the library out of the temporary directory
shutil.move('/temporary/directory/mymodel.dll', './mymodel.dll')

export_srcpkg(platform, toolchain, pkgpath, libname, params=None, compiler='ast_native', verbose=False, options=None)¶

Convenience function: Generate prediction code and create a zipped source package for deployment. The resulting zip file will also contain a Makefile.

Parameters

platform (str) – name of the operating system on which the headers and sources shall be compiled. Must be one of the following: ‘windows’ (Microsoft Windows), ‘osx’ (Mac OS X), ‘unix’ (Linux and other UNIX-like systems)
toolchain (str) – which toolchain to use. You may choose one of ‘msvc’, ‘clang’, ‘gcc’, and ‘cmake’. You may also specify a specific variation of clang or gcc (e.g. ‘gcc-7’)
pkgpath (str) – location to save the zipped source package
libname (str) – name of model shared library to be built
params (dict, optional) – parameters to be passed to the compiler. See this page for the list of compiler parameters.
compiler (str, optional) – name of compiler to use in C code generation
verbose (bool, optional) – whether to produce extra messages
nthread (int, optional) – number of threads to use in creating the shared library. Defaults to the number of cores in the system.
options (list of str, optional) – Additional options to pass to toolchain

Example

The one-line command

model.export_srcpkg(platform='unix', toolchain='gcc',
                    pkgpath='./mymodel_pkg.zip', libname='mymodel.so',
                    params={}, verbose=True)

is equivalent to the following sequence of commands:

model.compile(dirpath='/temporary/directory/mymodel',
              params={}, verbose=True)
generate_makefile(dirpath='/temporary/directory/mymodel',
                  platform='unix', toolchain='gcc')
# zip the directory containing C code and Makefile
shutil.make_archive(base_name=pkgpath, format='zip',
                    root_dir='/temporary/directory',
                    base_dir='mymodel/')

classmethod from_xgboost(booster)¶

Load a tree ensemble model from an XGBoost Booster object

Parameters: booster (object of type xgboost.Booster) – Python handle to XGBoost model
Returns: model – loaded model
Return type: Model object

Example

bst = xgboost.train(params, dtrain, 10, [(dtrain, 'train')])
xgb_model = Model.from_xgboost(bst)

classmethod from_xgboost_json(json_str)¶

Load a tree ensemble model from a string containing XGBoost JSON

Parameters: json_str (a string specifying an XGBoost model in the XGBoost JSON) – format
Returns: model – loaded model
Return type: Model object

Example

bst = xgboost.train(params, dtrain, 10, [(dtrain, 'train')])
bst.save_model('model.json')
with open('model.json') as file_:
    json_str = file_.read()
xgb_model = Model.from_xgboost_json(json_str)

classmethod load(filename, model_format)¶

Load a tree ensemble model from a file

Parameters

filename (str) – path to model file
model_format (str) – model file format. Must be ‘xgboost’, ‘xgboost_json’, or ‘lightgbm’

Returns

model – loaded model

Return type

Model object

Example

xgb_model = Model.load('xgboost_model.model', 'xgboost')

property num_class¶: Number of classes of the model (1 if the model is not a multi-class classifier

property num_feature¶: Number of features used in the model

property num_tree¶: Number of decision trees in the model

serialize(filename)¶

Serialize (persist) the model to a checkpoint file in the disk, using a fast binary representation. To recover the model from the checkpoint, use deserialize() method.

Note

Use exactly matching versions of Treelite when exchanging checkpoints

We provide ZERO backward compatibility guarantee. You will not be able to recover the model from a checkpoint that was generated by a previous version of Treelite. Both the producer and the consumer of the checkpoint must have the identical major and minor versions of Treelite.

Parameters: filename (str) – Path to checkpoint

set_tree_limit(tree_limit)¶: Set first n trees to be kept, the remaining ones will be dropped

class treelite.ModelBuilder(num_feature, num_class=1, average_tree_output=False, threshold_type='float32', leaf_output_type='float32', **kwargs)¶

Builder class for tree ensemble model: provides tools to iteratively build an ensemble of decision trees

Parameters

num_feature (int) – number of features used in model being built. We assume that all feature indices are between 0 and (num_feature - 1)
num_class (int, optional) – number of output groups; >1 indicates multiclass classification
average_tree_output (bool, optional) – whether the model is a random forest; True indicates a random forest and False indicates gradient boosted trees
**kwargs – model parameters, to be used to specify the resulting model. Refer to this page for the full list of model parameters.

class Node¶

Handle to a node in a tree

set_categorical_test_node(feature_id, left_categories, default_left, left_child_key, right_child_key)¶

Set the node as a test node with categorical split. A list defines all categories that would be classified as the left side. Categories are integers ranging from 0 to n-1, where n is the number of categories in that particular feature.

Parameters

feature_id (int) – feature index
left_categories (list of int) – list of categories belonging to the left child.
default_left (bool) – default direction for missing values (True for left; False for right)
left_child_key (int) – unique integer key to identify the left child node
right_child_key (int) – unique integer key to identify the right child node

set_leaf_node(leaf_value, leaf_value_type='float32')¶

Set the node as a leaf node

Parameters

leaf_value (float / list of float) – Usually a single leaf value (weight) of the leaf node. For multiclass random forest classifier, leaf_value should be a list of leaf weights.
leaf_value_type (str) – Data type used for leaf_value (e.g. ‘float32’)

set_numerical_test_node(feature_id, opname, threshold, default_left, left_child_key, right_child_key, threshold_type='float32')¶

Set the node as a test node with numerical split. The test is in the form [feature value] OP [threshold]. Depending on the result of the test, either left or right child would be taken.

Parameters

feature_id (int) – feature index
opname (str) – binary operator to use in the test
threshold (float) – threshold value
default_left (bool) – default direction for missing values (True for left; False for right)
left_child_key (int) – unique integer key to identify the left child node
right_child_key (int) – unique integer key to identify the right child node
threshold_type (str) – data type for threshold value (e.g. ‘float32’)

set_root()¶: Set the node as the root

class Tree(threshold_type='float32', leaf_output_type='float32')¶: Handle to a decision tree in a tree ensemble Builder

class Value(init_value, dtype)¶

Value whose type may be specified at runtime

Parameters: dtype (str) – Initial value of model handle

append(tree)¶

Add a tree at the end of the ensemble

Parameters: tree (Tree object) – tree to be added

Example

builder = ModelBuilder(num_feature=4227)
tree = ...               # build tree somehow
builder.append(tree)     # add tree at the end of the ensemble

commit()¶

Finalize the ensemble model

Returns: model – finished model
Return type: Model object

Example

builder = ModelBuilder(num_feature=4227)
for i in range(100):
  tree = ...                    # build tree somehow
  builder.append(tree)          # add one tree at a time

model = builder.commit()        # now get a Model object
model.compile(dirpath='test')   # compile model into C code

insert(index, tree)¶

Insert a tree at specified location in the ensemble

Parameters

index (int) – index of the element before which to insert the tree
tree (Tree object) – tree to be inserted

Example

builder = ModelBuilder(num_feature=4227)
tree = ...               # build tree somehow
builder.insert(0, tree)  # insert tree at index 0

class treelite.Annotator¶

Branch annotator class: annotate branches in a given model using frequency patterns in the training data

annotate_branch(model, dmat, nthread=None, verbose=False)¶

Annotate branches in a given model using frequency patterns in the training data. Each node gets the count of the instances that belong to it. Any prior annotation information stored in the annotator will be replaced with the new annotation returned by this method.

Parameters

model (object of type Model) – decision tree ensemble model
dmat (object of type DMatrix) – data matrix representing the training data
nthread (int, optional) – number of threads to use while annotating. If missing, use all physical cores in the system.
verbose (bool, optional) – whether to produce extra messages

save(path)¶

Save branch annotation infromation as a JSON file.

Parameters: path (str) – location of saved JSON file

treelite.create_shared(toolchain, dirpath, nthread=None, verbose=False, options=None)¶

Create shared library.

Parameters

toolchain (str) – which toolchain to use. You may choose one of ‘msvc’, ‘clang’, and ‘gcc’. You may also specify a specific variation of clang or gcc (e.g. ‘gcc-7’)
dirpath (str) – directory containing the header and source files previously generated by Model.compile(). The directory must contain recipe.json which specifies build dependencies.
nthread (int, optional) – number of threads to use in creating the shared library. Defaults to the number of cores in the system.
verbose (bool, optional) – whether to produce extra messages
options (list of str, optional) – Additional options to pass to toolchain

Returns

libpath – absolute path of created shared library

Return type

str

Example

The following command uses Visual C++ toolchain to generate ./my/model/model.dll:

model.compile(dirpath='./my/model', params={}, verbose=True)
create_shared(toolchain='msvc', dirpath='./my/model', verbose=True)

Later, the shared library can be referred to by its directory name:

predictor = Predictor(libpath='./my/model', verbose=True)
# looks for ./my/model/model.dll

Alternatively, one may specify the library down to its file name:

predictor = Predictor(libpath='./my/model/model.dll', verbose=True)

treelite.generate_makefile(dirpath, platform, toolchain, options=None)¶

Generate a Makefile for a given directory of headers and sources. The resulting Makefile will be stored in the directory. This function is useful for deploying a model on a different machine.

Parameters

dirpath (str) – directory containing the header and source files previously generated by Model.compile(). The directory must contain recipe.json which specifies build dependencies.
platform (str) – name of the operating system on which the headers and sources shall be compiled. Must be one of the following: ‘windows’ (Microsoft Windows), ‘osx’ (Mac OS X), ‘unix’ (Linux and other UNIX-like systems)
toolchain (str) – which toolchain to use. You may choose one of ‘msvc’, ‘clang’, and ‘gcc’. You may also specify a specific variation of clang or gcc (e.g. ‘gcc-7’)
options (list of str, optional) – Additional options to pass to toolchain

treelite.generate_cmakelists(dirpath, options=None)¶

Generate a CMakeLists.txt for a given directory of headers and sources. The resulting CMakeLists.txt will be stored in the directory. This function is useful for deploying a model on a different machine.

Parameters

dirpath (str) – directory containing the header and source files previously generated by Model.compile(). The directory must contain recipe.json which specifies build dependencies.
options (list of str, optional) – Additional options to pass to toolchain

exception treelite.TreeliteError¶: Error thrown by Treelite

Converter to ingest scikit-learn models into Treelite

treelite.sklearn.import_model_with_model_builder(sklearn_model)¶

Load a tree ensemble model from a scikit-learn model object using the model builder API.

Note

Use import_model for production use

This function exists to demonstrate the use of the model builder API and is slow with large models. For production, please use import_model() which is significantly faster.

Parameters: sklearn_model (object of type RandomForestRegressor / RandomForestClassifier / ExtraTreesRegressor / ExtraTreesClassifier / GradientBoostingRegressor / GradientBoostingClassifier) – Python handle to scikit-learn model
Returns: model – loaded model
Return type: Model object

Example

import sklearn.datasets
import sklearn.ensemble
X, y = sklearn.datasets.load_boston(return_X_y=True)
clf = sklearn.ensemble.RandomForestRegressor(n_estimators=10)
clf.fit(X, y)

import treelite.sklearn
model = treelite.sklearn.import_model_with_model_builder(clf)

treelite.sklearn.import_model(sklearn_model)¶

Load a tree ensemble model from a scikit-learn model object

Parameters: sklearn_model (object of type RandomForestRegressor / RandomForestClassifier / ExtraTreesRegressor / ExtraTreesClassifier / GradientBoostingRegressor / GradientBoostingClassifier) – Python handle to scikit-learn model
Returns: model – loaded model
Return type: Model object

Example

import sklearn.datasets
import sklearn.ensemble
X, y = sklearn.datasets.load_boston(return_X_y=True)
clf = sklearn.ensemble.RandomForestRegressor(n_estimators=10)
clf.fit(X, y)

import treelite.sklearn
model = treelite.sklearn.import_model(clf)

Table Of Contents

Treelite API¶