Note
Go to the end to download the full example code
Logging, verbose#
The conversion of a pipeline fails if it contains an object without any associated converter. It may also fails if one of the object is mapped by a custom converter. If the error message is not explicit enough, it is possible to enable logging.
Train a model#
A very basic example using random forest and the iris dataset.
import logging
import numpy
import onnx
import onnxruntime as rt
import sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from skl2onnx.common.data_types import FloatTensorType
from skl2onnx import convert_sklearn
import skl2onnx
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = DecisionTreeClassifier()
clr.fit(X_train, y_train)
print(clr)
DecisionTreeClassifier()
Convert a model into ONNX#
initial_type = [('float_input', FloatTensorType([None, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type,
target_opset=12)
sess = rt.InferenceSession(onx.SerializeToString())
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
pred_onx = sess.run([label_name],
{input_name: X_test.astype(numpy.float32)})[0]
print(pred_onx)
[2 2 2 1 2 1 0 1 2 2 2 2 0 2 2 2 1 0 1 1 0 0 0 1 1 0 1 0 0 1 1 2 0 0 1 0 1
0]
Conversion with parameter verbose#
verbose is a parameter which prints messages on the standard output. It tells which converter is called. verbose=1 usually means what skl2onnx is doing to convert a pipeline. verbose=2+ is reserved for information within converters.
convert_sklearn(clr, initial_types=initial_type, target_opset=12, verbose=1)
[convert_sklearn] parse_sklearn_model
[convert_sklearn] convert_topology
[convert_operators] begin
[convert_operators] iteration 1 - n_vars=0 n_ops=2
[call_converter] call converter for 'SklearnDecisionTreeClassifier'.
[call_converter] call converter for 'SklearnZipMap'.
[convert_operators] end iter: 1 - n_vars=5
[convert_operators] iteration 2 - n_vars=5 n_ops=2
[convert_operators] end iter: 2 - n_vars=5
[convert_operators] end.
[_update_domain_version] +opset 0: name='', version=9
[_update_domain_version] +opset 1: name='ai.onnx.ml', version=1
[convert_sklearn] end
ir_version: 7
producer_name: "skl2onnx"
producer_version: "1.14.0"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
node {
input: "float_input"
output: "label"
output: "probabilities"
name: "TreeEnsembleClassifier"
op_type: "TreeEnsembleClassifier"
attribute {
name: "class_ids"
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attribute {
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attribute {
name: "classlabels_int64s"
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attribute {
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attribute {
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attribute {
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attribute {
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attribute {
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floats: 0.0
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attribute {
name: "post_transform"
s: "NONE"
type: STRING
}
domain: "ai.onnx.ml"
}
node {
input: "probabilities"
output: "output_probability"
name: "ZipMap"
op_type: "ZipMap"
attribute {
name: "classlabels_int64s"
ints: 0
ints: 1
ints: 2
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}
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}
node {
input: "label"
output: "output_label"
name: "Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
name: "deb1b65745284802904d0b7988deb9d2"
input {
name: "float_input"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 4
}
}
}
}
}
output {
name: "output_label"
type {
tensor_type {
elem_type: 7
shape {
dim {
}
}
}
}
}
output {
name: "output_probability"
type {
sequence_type {
elem_type {
map_type {
key_type: 7
value_type {
tensor_type {
elem_type: 1
}
}
}
}
}
}
}
}
opset_import {
domain: ""
version: 9
}
opset_import {
domain: "ai.onnx.ml"
version: 1
}
Conversion with logging#
This is very detailed logging. It which operators or variables (output of converters) is processed, which node is created… This information may be useful when a custom converter is being implemented.
logger = logging.getLogger('skl2onnx')
logger.setLevel(logging.DEBUG)
logging.basicConfig(level=logging.DEBUG)
convert_sklearn(clr, initial_types=initial_type, target_opset=12)
DEBUG:skl2onnx:[Var] +Variable('float_input', 'float_input', type=FloatTensorType(shape=[None, 4]))
DEBUG:skl2onnx:[Var] update is_root=True for Variable('float_input', 'float_input', type=FloatTensorType(shape=[None, 4]))
DEBUG:skl2onnx:[parsing] found alias='SklearnDecisionTreeClassifier' for type=<class 'sklearn.tree._classes.DecisionTreeClassifier'>.
DEBUG:skl2onnx:[Op] +Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='', outputs='', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Op] add In Variable('float_input', 'float_input', type=FloatTensorType(shape=[None, 4])) to Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Var] +Variable('label', 'label', type=Int64TensorType(shape=[]))
DEBUG:skl2onnx:[Var] +Variable('probabilities', 'probabilities', type=FloatTensorType(shape=[]))
DEBUG:skl2onnx:[Var] set parent for Variable('label', 'label', type=Int64TensorType(shape=[])), parent=Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Op] add Out Variable('label', 'label', type=Int64TensorType(shape=[])) to Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Var] set parent for Variable('probabilities', 'probabilities', type=FloatTensorType(shape=[])), parent=Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Op] add Out Variable('probabilities', 'probabilities', type=FloatTensorType(shape=[])) to Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Op] +Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='', outputs='', raw_operator=None)
DEBUG:skl2onnx:[Var] +Variable('output_label', 'output_label', type=Int64TensorType(shape=[None]))
DEBUG:skl2onnx:[Var] set parent for Variable('output_label', 'output_label', type=Int64TensorType(shape=[None])), parent=Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='', raw_operator=None)
DEBUG:skl2onnx:[Op] add Out Variable('output_label', 'output_label', type=Int64TensorType(shape=[None])) to Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label', raw_operator=None)
DEBUG:skl2onnx:[Var] +Variable('output_probability', 'output_probability', type=SequenceType(element_type=DictionaryType(key_type=Int64TensorType(shape=[None]), value_type=FloatTensorType(shape=[]))))
DEBUG:skl2onnx:[Var] set parent for Variable('output_probability', 'output_probability', type=SequenceType(element_type=DictionaryType(key_type=Int64TensorType(shape=[None]), value_type=FloatTensorType(shape=[])))), parent=Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label', raw_operator=None)
DEBUG:skl2onnx:[Op] add Out Variable('output_probability', 'output_probability', type=SequenceType(element_type=DictionaryType(key_type=Int64TensorType(shape=[None]), value_type=FloatTensorType(shape=[])))) to Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Op] update is_evaluated=True for Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Var] update is_leaf=True for Variable('output_label', 'output_label', type=Int64TensorType(shape=[None]))
DEBUG:skl2onnx:[Var] update is_leaf=True for Variable('output_probability', 'output_probability', type=SequenceType(element_type=DictionaryType(key_type=Int64TensorType(shape=[None]), value_type=FloatTensorType(shape=[]))))
DEBUG:skl2onnx:[Var] update is_fed=True for Variable('float_input', 'float_input', type=FloatTensorType(shape=[None, 4])), parent=None
DEBUG:skl2onnx:[Var] update is_fed=False for Variable('label', 'label', type=Int64TensorType(shape=[])), parent=Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Var] update is_fed=False for Variable('probabilities', 'probabilities', type=FloatTensorType(shape=[])), parent=Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Var] update is_fed=False for Variable('output_label', 'output_label', type=Int64TensorType(shape=[None])), parent=Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Var] update is_fed=False for Variable('output_probability', 'output_probability', type=SequenceType(element_type=DictionaryType(key_type=Int64TensorType(shape=[None]), value_type=FloatTensorType(shape=[])))), parent=Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Op] update is_evaluated=False for Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Op] update is_evaluated=False for Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Shape2] call infer_types for Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Shape-a] Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier()) fed 'True' - 'FalseFalse'
DEBUG:skl2onnx:[Var] update type for Variable('label', 'label', type=Int64TensorType(shape=[]))
DEBUG:skl2onnx:[Shape-b] Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier()) inputs=[Variable('float_input', 'float_input', type=FloatTensorType(shape=[None, 4]))] - outputs=[Variable('label', 'label', type=Int64TensorType(shape=[None])), Variable('probabilities', 'probabilities', type=FloatTensorType(shape=[None, 3]))]
DEBUG:skl2onnx:[Conv] call Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier()) fed 'True' - 'FalseFalse'
DEBUG:skl2onnx:[Node] 'TreeEnsembleClassifier' - 'float_input' -> 'label,probabilities' (name='TreeEnsembleClassifier')
DEBUG:skl2onnx:[Conv] end - Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Op] update is_evaluated=True for Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Var] update is_fed=True for Variable('label', 'label', type=Int64TensorType(shape=[None])), parent=Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Var] update is_fed=True for Variable('probabilities', 'probabilities', type=FloatTensorType(shape=[None, 3])), parent=Operator(type='SklearnDecisionTreeClassifier', onnx_name='SklearnDecisionTreeClassifier', inputs='float_input', outputs='label,probabilities', raw_operator=DecisionTreeClassifier())
DEBUG:skl2onnx:[Shape2] call infer_types for Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Shape-a] Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None) fed 'TrueTrue' - 'FalseFalse'
DEBUG:skl2onnx:[Var] update type for Variable('output_label', 'output_label', type=Int64TensorType(shape=[None]))
DEBUG:skl2onnx:[Shape-b] Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None) inputs=[Variable('label', 'label', type=Int64TensorType(shape=[None])), Variable('probabilities', 'probabilities', type=FloatTensorType(shape=[None, 3]))] - outputs=[Variable('output_label', 'output_label', type=Int64TensorType(shape=[None])), Variable('output_probability', 'output_probability', type=SequenceType(element_type=DictionaryType(key_type=Int64TensorType(shape=[None]), value_type=FloatTensorType(shape=[]))))]
DEBUG:skl2onnx:[Conv] call Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None) fed 'TrueTrue' - 'FalseFalse'
DEBUG:skl2onnx:[Node] 'Cast' - 'label' -> 'output_label' (name='Cast')
DEBUG:skl2onnx:[Node] 'ZipMap' - 'probabilities' -> 'output_probability' (name='ZipMap')
DEBUG:skl2onnx:[Conv] end - Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Op] update is_evaluated=True for Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Var] update is_fed=True for Variable('output_label', 'output_label', type=Int64TensorType(shape=[None])), parent=Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
DEBUG:skl2onnx:[Var] update is_fed=True for Variable('output_probability', 'output_probability', type=SequenceType(element_type=DictionaryType(key_type=Int64TensorType(shape=[None]), value_type=FloatTensorType(shape=[])))), parent=Operator(type='SklearnZipMap', onnx_name='SklearnZipMap', inputs='label,probabilities', outputs='output_label,output_probability', raw_operator=None)
ir_version: 7
producer_name: "skl2onnx"
producer_version: "1.14.0"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
node {
input: "float_input"
output: "label"
output: "probabilities"
name: "TreeEnsembleClassifier"
op_type: "TreeEnsembleClassifier"
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name: "class_ids"
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attribute {
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attribute {
name: "classlabels_int64s"
ints: 0
ints: 1
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type: INTS
}
attribute {
name: "nodes_falsenodeids"
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attribute {
name: "nodes_hitrates"
floats: 1.0
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strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
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attribute {
name: "nodes_nodeids"
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attribute {
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attribute {
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floats: 4.849999904632568
floats: 4.949999809265137
floats: 3.8999998569488525
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floats: 1.75
floats: 4.949999809265137
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attribute {
name: "post_transform"
s: "NONE"
type: STRING
}
domain: "ai.onnx.ml"
}
node {
input: "label"
output: "output_label"
name: "Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "probabilities"
output: "output_probability"
name: "ZipMap"
op_type: "ZipMap"
attribute {
name: "classlabels_int64s"
ints: 0
ints: 1
ints: 2
type: INTS
}
domain: "ai.onnx.ml"
}
name: "d4e54234eafe4b21ad1417b49809c723"
input {
name: "float_input"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 4
}
}
}
}
}
output {
name: "output_label"
type {
tensor_type {
elem_type: 7
shape {
dim {
}
}
}
}
}
output {
name: "output_probability"
type {
sequence_type {
elem_type {
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value_type {
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opset_import {
domain: ""
version: 9
}
opset_import {
domain: "ai.onnx.ml"
version: 1
}
And to disable it.
logger.setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO)
convert_sklearn(clr, initial_types=initial_type, target_opset=12)
ir_version: 7
producer_name: "skl2onnx"
producer_version: "1.14.0"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
node {
input: "float_input"
output: "label"
output: "probabilities"
name: "TreeEnsembleClassifier"
op_type: "TreeEnsembleClassifier"
attribute {
name: "class_ids"
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}
attribute {
name: "class_nodeids"
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}
attribute {
name: "class_treeids"
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}
attribute {
name: "class_weights"
floats: 1.0
floats: 0.0
floats: 0.0
floats: 0.0
floats: 1.0
floats: 0.0
floats: 0.0
floats: 0.0
floats: 1.0
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floats: 1.0
floats: 0.0
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floats: 1.0
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floats: 1.0
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floats: 0.0
floats: 1.0
type: FLOATS
}
attribute {
name: "classlabels_int64s"
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ints: 1
ints: 2
type: INTS
}
attribute {
name: "nodes_falsenodeids"
ints: 2
ints: 0
ints: 8
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ints: 6
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}
attribute {
name: "nodes_featureids"
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}
attribute {
name: "nodes_hitrates"
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
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floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
type: FLOATS
}
attribute {
name: "nodes_missing_value_tracks_true"
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}
attribute {
name: "nodes_modes"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
type: STRINGS
}
attribute {
name: "nodes_nodeids"
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}
attribute {
name: "nodes_treeids"
ints: 0
ints: 0
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}
attribute {
name: "nodes_truenodeids"
ints: 1
ints: 0
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ints: 4
ints: 5
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ints: 10
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}
attribute {
name: "nodes_values"
floats: 2.5999999046325684
floats: 0.0
floats: 4.849999904632568
floats: 4.949999809265137
floats: 3.8999998569488525
floats: 0.0
floats: 0.0
floats: 0.0
floats: 1.75
floats: 4.949999809265137
floats: 0.0
floats: 1.5499999523162842
floats: 0.0
floats: 6.949999809265137
floats: 0.0
floats: 0.0
floats: 0.0
type: FLOATS
}
attribute {
name: "post_transform"
s: "NONE"
type: STRING
}
domain: "ai.onnx.ml"
}
node {
input: "label"
output: "output_label"
name: "Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "probabilities"
output: "output_probability"
name: "ZipMap"
op_type: "ZipMap"
attribute {
name: "classlabels_int64s"
ints: 0
ints: 1
ints: 2
type: INTS
}
domain: "ai.onnx.ml"
}
name: "6f56fd8fc39d404ab00fa8fdb2117ac8"
input {
name: "float_input"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 4
}
}
}
}
}
output {
name: "output_label"
type {
tensor_type {
elem_type: 7
shape {
dim {
}
}
}
}
}
output {
name: "output_probability"
type {
sequence_type {
elem_type {
map_type {
key_type: 7
value_type {
tensor_type {
elem_type: 1
}
}
}
}
}
}
}
}
opset_import {
domain: ""
version: 9
}
opset_import {
domain: "ai.onnx.ml"
version: 1
}
Versions used for this example
print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", rt.__version__)
print("skl2onnx: ", skl2onnx.__version__)
numpy: 1.23.5
scikit-learn: 1.2.2
onnx: 1.13.1
onnxruntime: 1.14.1
skl2onnx: 1.14.0
Total running time of the script: ( 0 minutes 0.243 seconds)