def make_annotation(model, dtrain_path, annotation_path):
dpath = os.path.abspath(os.path.join(os.getcwd(), 'tests/examples/'))
dtrain_path = os.path.join(dpath, dtrain_path)
dtrain = treelite.DMatrix(dtrain_path)
annotator = treelite.Annotator()
annotator.annotate_branch(model=model, dmat=dtrain, verbose=True)
annotator.save(path=annotation_path)
def test_skl_converter_iforest(tmpdir, toolchain): # pylint: disable=W0212
# pylint: disable=too-many-locals
"""Convert scikit-learn regressor"""
X = load_boston(return_X_y=True)[0]
clf = IsolationForest(max_samples=64, random_state=0, n_estimators=10)
clf.fit(X)
expected_pred = clf._compute_chunked_score_samples(X) # pylint: disable=W0212
model = treelite.sklearn.import_model(clf)
assert model.num_feature == clf.n_features_
assert model.num_class == 1
assert model.num_tree == clf.n_estimators
dtrain = treelite_runtime.DMatrix(X, dtype='float32')
annotation_path = os.path.join(tmpdir, 'annotation.json')
annotator = treelite.Annotator()
annotator.annotate_branch(model=model, dmat=dtrain, verbose=True)
annotator.save(path=annotation_path)
libpath = os.path.join(tmpdir, 'skl' + _libext())
model.export_lib(toolchain=toolchain,
libpath=libpath,
params={'annotate_in': annotation_path},
verbose=True)
predictor = treelite_runtime.Predictor(libpath=libpath)
assert predictor.num_feature == clf.n_features_
assert model.num_class == 1
assert predictor.pred_transform == 'exponential_standard_ratio'
assert predictor.global_bias == 0.0
assert predictor.sigmoid_alpha == 1.0
assert predictor.ratio_c > 0
assert predictor.ratio_c < 64
out_pred = predictor.predict(dtrain)
np.testing.assert_almost_equal(out_pred, expected_pred, decimal=2)
def test_skl_converter_regressor(tmpdir, clazz, toolchain): # pylint: disable=too-many-locals
"""Convert scikit-learn regressor"""
X, y = load_boston(return_X_y=True)
kwargs = {}
if clazz == GradientBoostingRegressor:
kwargs['init'] = 'zero'
clf = clazz(max_depth=3, random_state=0, n_estimators=10, **kwargs)
clf.fit(X, y)
expected_pred = clf.predict(X)
model = treelite.sklearn.import_model(clf)
assert model.num_feature == clf.n_features_
assert model.num_output_group == 1
assert model.num_tree == clf.n_estimators
dtrain = treelite_runtime.DMatrix(X, dtype='float32')
annotation_path = os.path.join(tmpdir, 'annotation.json')
annotator = treelite.Annotator()
annotator.annotate_branch(model=model, dmat=dtrain, verbose=True)
annotator.save(path=annotation_path)
libpath = os.path.join(tmpdir, 'skl' + _libext())
model.export_lib(toolchain=toolchain,
libpath=libpath,
params={'annotate_in': annotation_path},
verbose=True)
predictor = treelite_runtime.Predictor(libpath=libpath)
assert predictor.num_feature == clf.n_features_
assert model.num_output_group == 1
assert predictor.pred_transform == 'identity'
assert predictor.global_bias == 0.0
assert predictor.sigmoid_alpha == 1.0
out_pred = predictor.predict(dtrain)
np.testing.assert_almost_equal(out_pred, expected_pred, decimal=5)
def compute_annotation(dataset):
model = treelite.Model.load(
dataset_db[dataset].model,
model_format=dataset_db[dataset].format)
if dataset_db[dataset].dtrain is None:
return None
dtrain = treelite_runtime.DMatrix(dataset_db[dataset].dtrain)
annotator = treelite.Annotator()
annotator.annotate_branch(model=model, dmat=dtrain, verbose=True)
annotation_path = os.path.join(tmpdir, f'{dataset}.json')
annotator.save(annotation_path)
with open(annotation_path, 'r') as f:
return f.read()
def test_skl_converter_multiclass_classifier(tmpdir, clazz, toolchain):
# pylint: disable=too-many-locals
"""Convert scikit-learn multi-class classifier"""
if clazz == RandomForestClassifier:
pytest.xfail(
reason='Need to use float64 thresholds to obtain correct result')
X, y = load_iris(return_X_y=True)
kwargs = {}
if clazz == GradientBoostingClassifier:
kwargs['init'] = 'zero'
clf = clazz(max_depth=3, random_state=0, n_estimators=10, **kwargs)
clf.fit(X, y)
expected_prob = clf.predict_proba(X)
model = treelite.sklearn.import_model(clf)
assert model.num_feature == clf.n_features_
assert model.num_output_group == clf.n_classes_
assert (model.num_tree == clf.n_estimators *
(clf.n_classes_ if clazz == GradientBoostingClassifier else 1))
dtrain = treelite.DMatrix(X)
annotation_path = os.path.join(tmpdir, 'annotation.json')
annotator = treelite.Annotator()
annotator.annotate_branch(model=model, dmat=dtrain, verbose=True)
annotator.save(path=annotation_path)
libpath = os.path.join(tmpdir, 'skl' + _libext())
model.export_lib(toolchain=toolchain,
libpath=libpath,
params={'annotate_in': annotation_path},
verbose=True)
predictor = treelite_runtime.Predictor(libpath=libpath)
assert predictor.num_feature == clf.n_features_
assert predictor.num_output_group == clf.n_classes_
assert (predictor.pred_transform == ('softmax'
if clazz == GradientBoostingClassifier
else 'identity_multiclass'))
assert predictor.global_bias == 0.0
assert predictor.sigmoid_alpha == 1.0
batch = treelite_runtime.Batch.from_npy2d(X)
out_prob = predictor.predict(batch)
np.testing.assert_almost_equal(out_prob, expected_prob)
def test_skl_converter_multiclass_classifier(tmpdir, import_method, clazz,
toolchain):
# pylint: disable=too-many-locals
"""Convert scikit-learn multi-class classifier"""
X, y = load_iris(return_X_y=True)
kwargs = {}
if clazz == GradientBoostingClassifier:
kwargs['init'] = 'zero'
clf = clazz(max_depth=3, random_state=0, n_estimators=10, **kwargs)
clf.fit(X, y)
expected_prob = clf.predict_proba(X)
if import_method == 'import_new':
model = treelite.sklearn.import_model(clf)
else:
model = treelite.sklearn.import_model_with_model_builder(clf)
assert model.num_feature == clf.n_features_
assert model.num_class == clf.n_classes_
assert (model.num_tree == clf.n_estimators *
(clf.n_classes_ if clazz == GradientBoostingClassifier else 1))
dtrain = treelite_runtime.DMatrix(X, dtype='float64')
annotation_path = os.path.join(tmpdir, 'annotation.json')
annotator = treelite.Annotator()
annotator.annotate_branch(model=model, dmat=dtrain, verbose=True)
annotator.save(path=annotation_path)
libpath = os.path.join(tmpdir, 'skl' + _libext())
model.export_lib(toolchain=toolchain,
libpath=libpath,
params={'annotate_in': annotation_path},
verbose=True)
predictor = treelite_runtime.Predictor(libpath=libpath)
assert predictor.num_feature == clf.n_features_
assert predictor.num_class == clf.n_classes_
assert (predictor.pred_transform == ('softmax'
if clazz == GradientBoostingClassifier
else 'identity_multiclass'))
assert predictor.global_bias == 0.0
assert predictor.sigmoid_alpha == 1.0
out_prob = predictor.predict(dtrain)
np.testing.assert_almost_equal(out_prob, expected_prob)
import xgboost
from fast_svmlight_loader import load_svmlight
import sys
import os
import numpy as np
import time
files = [('allstate/allstate0.train.libsvm', 'allstate/allstate.json',
'allstate/allstate.so', 'allstate/allstate-1600-0.010000.model'),
('yahoo/yahoo.train', 'yahoo/yahoo.json', 'yahoo/yahoo.so',
'yahoo/yahoo-1600-0.01.model')]
for datafile, annotfile, libfile, modfile in files:
model = treelite.Model.load(modfile, 'xgboost')
dmat = treelite.DMatrix(datafile)
annotator = treelite.Annotator()
annotator.annotate_branch(model, dmat, verbose=True)
annotator.save(annotfile)
model.export_lib(toolchain='gcc',
libpath=libfile,
params={
'quantize': 1,
'annotate_in': annotfile
},
verbose=True)
for datafile, _, libfile, modfile in files:
print('Loading data file {}'.format(datafile))
start = time.time()
dmat = load_svmlight(filename=datafile, verbose=False)
end = time.time()
def test_model_builder(tmpdir, use_annotation, quantize, toolchain,
test_round_trip):
# pylint: disable=R0914,R0915
"""A simple model"""
num_feature = 127
pred_transform = 'sigmoid'
builder = treelite.ModelBuilder(num_feature=num_feature,
average_tree_output=False,
pred_transform=pred_transform)
# Build mushroom model
tree = treelite.ModelBuilder.Tree()
tree[0].set_numerical_test_node(feature_id=29,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=1,
right_child_key=2)
tree[1].set_numerical_test_node(feature_id=56,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=3,
right_child_key=4)
tree[3].set_numerical_test_node(feature_id=60,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=7,
right_child_key=8)
tree[7].set_leaf_node(leaf_value=1.89899647)
tree[8].set_leaf_node(leaf_value=-1.94736838)
tree[4].set_numerical_test_node(feature_id=21,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=9,
right_child_key=10)
tree[9].set_leaf_node(leaf_value=1.78378379)
tree[10].set_leaf_node(leaf_value=-1.98135197)
tree[2].set_numerical_test_node(feature_id=109,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=5,
right_child_key=6)
tree[5].set_numerical_test_node(feature_id=67,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=11,
right_child_key=12)
tree[11].set_leaf_node(leaf_value=-1.9854598)
tree[12].set_leaf_node(leaf_value=0.938775539)
tree[6].set_leaf_node(leaf_value=1.87096775)
tree[0].set_root()
builder.append(tree)
tree = treelite.ModelBuilder.Tree()
tree[0].set_numerical_test_node(feature_id=29,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=1,
right_child_key=2)
tree[1].set_numerical_test_node(feature_id=21,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=3,
right_child_key=4)
tree[3].set_leaf_node(leaf_value=1.14607906)
tree[4].set_numerical_test_node(feature_id=36,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=7,
right_child_key=8)
tree[7].set_leaf_node(leaf_value=-6.87994671)
tree[8].set_leaf_node(leaf_value=-0.10659159)
tree[2].set_numerical_test_node(feature_id=109,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=5,
right_child_key=6)
tree[5].set_numerical_test_node(feature_id=39,
opname='<',
threshold=-9.53674316e-07,
default_left=True,
left_child_key=9,
right_child_key=10)
tree[9].set_leaf_node(leaf_value=-0.0930657759)
tree[10].set_leaf_node(leaf_value=-1.15261209)
tree[6].set_leaf_node(leaf_value=1.00423074)
tree[0].set_root()
builder.append(tree)
model = builder.commit()
if test_round_trip:
checkpoint_path = os.path.join(tmpdir, 'checkpoint.bin')
model.serialize(checkpoint_path)
model = treelite.Model.deserialize(checkpoint_path)
assert model.num_feature == num_feature
assert model.num_class == 1
assert model.num_tree == 2
annotation_path = os.path.join(tmpdir, 'annotation.json')
if use_annotation:
dtrain = treelite_runtime.DMatrix(load_svmlight_file(
dataset_db['mushroom'].dtrain, zero_based=True)[0],
dtype='float32')
annotator = treelite.Annotator()
annotator.annotate_branch(model=model, dmat=dtrain, verbose=True)
annotator.save(path=annotation_path)
params = {
'annotate_in': (annotation_path if use_annotation else 'NULL'),
'quantize': (1 if quantize else 0),
'parallel_comp': model.num_tree
}
libpath = os.path.join(tmpdir, 'mushroom' + _libext())
model.export_lib(toolchain=toolchain,
libpath=libpath,
params=params,
verbose=True)
predictor = treelite_runtime.Predictor(libpath=libpath, verbose=True)
check_predictor(predictor, 'mushroom')
