Note
Go to the end to download the full example code
Issues with FunctionTransformer#
A pipeline including a FunctionTransformer cannot be automatically converted into onnx because there is no converter able to convert custom python code into ONNX. A custom converter needs to be written specifically for it.
Initial try#
A very simple pipeline and the first attempt to convert it into ONNX.
import numpy as np
from numpy.testing import assert_allclose
from onnx.version_converter import convert_version
from pandas import DataFrame
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import FunctionTransformer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from skl2onnx import to_onnx
# For the custom converter
from skl2onnx import update_registered_converter
from skl2onnx.common.utils import check_input_and_output_numbers
from skl2onnx.algebra.onnx_ops import OnnxSlice, OnnxSub, OnnxDiv, OnnxMul, OnnxCastLike
from skl2onnx.helpers import add_onnx_graph
import onnxscript
from onnxscript import opset18 as op
# To check discrepancies
from onnx.reference import ReferenceEvaluator
from onnxruntime import InferenceSession
def calculate_growth(df):
df["c"] = 100 * (df["a"] - df["b"]) / df["b"]
return df
mapper = ColumnTransformer(
transformers=[
("c", FunctionTransformer(calculate_growth), ["a", "b"]),
],
remainder="passthrough",
verbose_feature_names_out=False,
)
mapper.set_output(transform="pandas")
pipe = Pipeline([("mapper", mapper), ("classifier", DecisionTreeClassifier())])
data = DataFrame(
[
dict(a=2, b=1, f=5),
dict(a=50, b=4, f=10),
dict(a=5, b=2, f=4),
dict(a=100, b=6, f=20),
]
)
y = np.array([0, 1, 0, 1], dtype=np.int64)
pipe.fit(data, y)
try:
to_onnx(pipe, data[:1], options={"zipmap": False})
except Exception as e:
print("It does not work:", e)
It does not work: FunctionTransformer is not supported unless the transform function is None (= identity). You may raise an issue at https://github.com/onnx/sklearn-onnx/issues.
Use of custom transformer#
It is easier to write a custom converter if the FunctionTransformer is implemented as a custom transformer.
class GrowthCalculator(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def calculate_growth(self, x, y):
return 100 * (x - y) / y
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
x = X.apply(lambda x: self.calculate_growth(x.a, x.b), axis=1)
return x.values.reshape((-1, 1))
mapper = ColumnTransformer(
transformers=[
("ab", FunctionTransformer(), ["a", "b"]), # We keep the first column.
("c", GrowthCalculator(), ["a", "b"]), # We add a new one.
],
remainder="passthrough",
verbose_feature_names_out=False,
)
pipe_tr = Pipeline([("mapper", mapper), ("classifier", DecisionTreeClassifier())])
pipe_tr.fit(data, y)
Both pipelines return the same output.
Let’s check it produces the same number of features.
assert_allclose(pipe.steps[0][-1].transform(data), pipe_tr.steps[0][-1].transform(data))
But the conversion still fails with a different error message.
It does not work: Unable to find a shape calculator for type '<class '__main__.GrowthCalculator'>'.
It usually means the pipeline being converted contains a
transformer or a predictor with no corresponding converter
implemented in sklearn-onnx. If the converted is implemented
in another library, you need to register
the converted so that it can be used by sklearn-onnx (function
update_registered_converter). If the model is not yet covered
by sklearn-onnx, you may raise an issue to
https://github.com/onnx/sklearn-onnx/issues
to get the converter implemented or even contribute to the
project. If the model is a custom model, a new converter must
be implemented. Examples can be found in the gallery.
Custom converter#
We need to implement the method calculate_growth in ONNX. The first function returns the expected type and shape.
def growth_shape_calculator(operator):
check_input_and_output_numbers(operator, input_count_range=1, output_count_range=1)
# Gets the input type, the transformer works on any numerical type.
input_type = operator.inputs[0].type.__class__
# The first dimension is usually dynamic (batch dimension).
input_dim = operator.inputs[0].get_first_dimension()
operator.outputs[0].type = input_type([input_dim, 1])
def growth_converter(scope, operator, container):
# No need to retrieve the fitted estimator, it is not trained.
# op = operator.raw_operator
opv = container.target_opset
X = operator.inputs[0]
# 100 * (x-y)/y --> 100 * (X[0] - X[1]) / X[1]
zero = np.array([0], dtype=np.int64)
one = np.array([1], dtype=np.int64)
two = np.array([2], dtype=np.int64)
hundred = np.array([100], dtype=np.float32)
# Slice(data, starts, ends, axes)
x0 = OnnxSlice(X, zero, one, one, op_version=opv)
x1 = OnnxSlice(X, one, two, one, op_version=opv)
z = OnnxMul(
OnnxCastLike(hundred, X, op_version=opv),
OnnxDiv(OnnxSub(x0, x1, op_version=opv), x1, op_version=opv),
op_version=opv,
output_names=operator.outputs[0],
)
z.add_to(scope, container)
update_registered_converter(
GrowthCalculator,
"AliasGrowthCalculator",
growth_shape_calculator,
growth_converter,
)
onx = to_onnx(pipe_tr, data[:1], target_opset=18, options={"zipmap": False})
Let’s check there is no discrepancies#
First the expected values
expected = (pipe_tr.predict(data), pipe_tr.predict_proba(data))
print(expected)
(array([0, 1, 0, 1]), array([[1., 0.],
[0., 1.],
[1., 0.],
[0., 1.]]))
Then let’s check with onnx.reference.ReferenceEvaluator.
feeds = {
"a": data["a"].values.reshape((-1, 1)),
"b": data["b"].values.reshape((-1, 1)),
"f": data["f"].values.reshape((-1, 1)),
}
# verbose=10 to show intermediate results
ref = ReferenceEvaluator(onx, verbose=0)
got = ref.run(None, feeds)
assert_allclose(expected[0], got[0])
assert_allclose(expected[1], got[1])
Then with the runtime used to deploy, onnxruntime for example.
ref = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])
got = ref.run(None, feeds)
assert_allclose(expected[0], got[0])
assert_allclose(expected[1], got[1])
Custom converter with onnxscript#
onnxscript offers a less verbose API than what onnx package implements. Let’s see how to use it to write the converters.
@onnxscript.script()
def calculate_onnxscript_verbose(X):
# onnxscript must define an opset. We use an identity node
# from a specific opset to set it (otherwise it fails).
x0 = op.Slice(X, [0], [1], [1])
x1 = op.Slice(X, [1], [2], [1])
return op.Mul(op.Div(op.Sub(x0, x1), x1), 100)
This version uses the strict definition of ONNX operators. The code can be more simple if regular python operators are used. They may not be converted into ONNX but an error message is raised in that case.
@onnxscript.script()
def calculate_onnxscript(X):
# onnxscript must define an opset. We use an identity node
# from a specific opset to set it (otherwise it fails).
xi = op.Identity(X)
x0 = xi[:, :1]
x1 = xi[:, 1:]
return (x0 - x1) / x1 * 100
We can also check that it is equivalent to the python implementation.
f_expected = calculate_growth(data)["c"].values
f_got = calculate_onnxscript(data[["a", "b"]].values.astype(np.float32))
assert_allclose(f_expected.ravel(), f_got.ravel(), atol=1e-6)
Let’s use it in the converter.
def growth_converter_onnxscript(scope, operator, container):
# No need to retrieve the fitted estimator, it is not trained.
# op = operator.raw_operator
opv = container.target_opset
# 100 * (x-y)/y --> 100 * (X[0] - X[1]) / X[1]
proto = calculate_onnxscript.to_model_proto()
# The function is written with opset 18, it needs to be converted
# to the opset required by the user when the conversion starts.
proto_version = convert_version(proto, opv)
add_onnx_graph(scope, operator, container, proto_version)
update_registered_converter(
GrowthCalculator,
"AliasGrowthCalculator",
growth_shape_calculator,
growth_converter_onnxscript,
)
Let’s check it works.
And again the discrepancies.
ref = ReferenceEvaluator(onx, verbose=0)
got = ref.run(None, feeds)
assert_allclose(expected[0], got[0])
assert_allclose(expected[1], got[1])
Finally.
print("done.")
done.
Total running time of the script: (0 minutes 0.270 seconds)