注意
转到末尾下载完整的示例代码。
逐步查看中间输出¶
我们重用示例转换包含ColumnTransformer的流水线，并逐步查看中间输出。转换后的模型很可能由于自定义转换器实现不正确而产生不同的输出或失败。一种方法是查看ONNX图中每个节点的输出。
创建并训练复杂的流水线¶
我们重用示例混合类型列转换器中实现的流水线。有一个改动，因为ONNX-ML Imputer不处理字符串类型。它不能成为最终ONNX流水线的一部分，必须被移除。请查找下方以---开头的注释。
import skl2onnx
import onnx
import sklearn
import matplotlib.pyplot as plt
import os
from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
from skl2onnx.helpers.onnx_helper import select_model_inputs_outputs
from skl2onnx.helpers.onnx_helper import save_onnx_model
from skl2onnx.helpers.onnx_helper import enumerate_model_node_outputs
from skl2onnx.helpers.onnx_helper import load_onnx_model
import numpy
import onnxruntime as rt
from skl2onnx import convert_sklearn
import pprint
from skl2onnx.common.data_types import (
FloatTensorType,
StringTensorType,
Int64TensorType,
)
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
titanic_url = (
"https://raw.githubusercontent.com/amueller/"
"scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv"
)
data = pd.read_csv(titanic_url)
X = data.drop("survived", axis=1)
y = data["survived"]
# SimpleImputer on string is not available
# for string in ONNX-ML specifications.
# So we do it beforehand.
for cat in ["embarked", "sex", "pclass"]:
X[cat].fillna("missing", inplace=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
numeric_features = ["age", "fare"]
numeric_transformer = Pipeline(
steps=[("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]
)
categorical_features = ["embarked", "sex", "pclass"]
categorical_transformer = Pipeline(
steps=[
# --- SimpleImputer is not available for strings in ONNX-ML specifications.
# ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
("onehot", OneHotEncoder(handle_unknown="ignore"))
]
)
preprocessor = ColumnTransformer(
transformers=[
("num", numeric_transformer, numeric_features),
("cat", categorical_transformer, categorical_features),
]
)
clf = Pipeline(
steps=[
("preprocessor", preprocessor),
("classifier", LogisticRegression(solver="lbfgs")),
]
)
clf.fit(X_train, y_train)
定义ONNX图的输入¶
sklearn-onnx不知道用于训练模型的特征，但它需要知道哪个特征具有哪个名称。我们只需重用dataframe列定义。
print(X_train.dtypes)
pclass int64
name object
sex object
age float64
sibsp int64
parch int64
ticket object
fare float64
cabin object
embarked object
boat object
body float64
home.dest object
dtype: object
转换后。
def convert_dataframe_schema(df, drop=None):
inputs = []
for k, v in zip(df.columns, df.dtypes):
if drop is not None and k in drop:
continue
if v == "int64":
t = Int64TensorType([None, 1])
elif v == "float64":
t = FloatTensorType([None, 1])
else:
t = StringTensorType([None, 1])
inputs.append((k, t))
return inputs
inputs = convert_dataframe_schema(X_train)
pprint.pprint(inputs)
[('pclass', Int64TensorType(shape=[None, 1])),
('name', StringTensorType(shape=[None, 1])),
('sex', StringTensorType(shape=[None, 1])),
('age', FloatTensorType(shape=[None, 1])),
('sibsp', Int64TensorType(shape=[None, 1])),
('parch', Int64TensorType(shape=[None, 1])),
('ticket', StringTensorType(shape=[None, 1])),
('fare', FloatTensorType(shape=[None, 1])),
('cabin', StringTensorType(shape=[None, 1])),
('embarked', StringTensorType(shape=[None, 1])),
('boat', StringTensorType(shape=[None, 1])),
('body', FloatTensorType(shape=[None, 1])),
('home.dest', StringTensorType(shape=[None, 1]))]
将单列合并为向量不是计算预测的最有效方法。这可以在将流水线转换为图之前完成。
将流水线转换为ONNX¶
try:
model_onnx = convert_sklearn(clf, "pipeline_titanic", inputs, target_opset=12)
except Exception as e:
print(e)
scikit-learn在可能的情况下会进行隐式转换。sklearn-onnx不会。ONNX版本的OneHotEncoder必须应用于同类型的列。
X_train["pclass"] = X_train["pclass"].astype(str)
X_test["pclass"] = X_test["pclass"].astype(str)
white_list = numeric_features + categorical_features
to_drop = [c for c in X_train.columns if c not in white_list]
inputs = convert_dataframe_schema(X_train, to_drop)
model_onnx = convert_sklearn(clf, "pipeline_titanic", inputs, target_opset=12)
# And save.
with open("pipeline_titanic.onnx", "wb") as f:
f.write(model_onnx.SerializeToString())
比较预测结果¶
最后一步，我们需要确保转换后的模型产生相同的预测、标签和概率。我们从scikit-learn开始。
print("predict", clf.predict(X_test[:5]))
print("predict_proba", clf.predict_proba(X_test[:1]))
predict [0 0 0 0 1]
predict_proba [[0.88600265 0.11399735]]
使用onnxruntime进行预测。我们需要移除被丢弃的列，并将双精度向量改为单精度向量，因为onnxruntime不支持双精度浮点数。onnxruntime不接受dataframe。输入必须以字典列表的形式给出。最后一个细节是，每一列实际上不是被描述为一个向量，而是被描述为一个单列矩阵，这解释了最后一行中的reshape操作。
X_test2 = X_test.drop(to_drop, axis=1)
inputs = {c: X_test2[c].values for c in X_test2.columns}
for c in numeric_features:
inputs[c] = inputs[c].astype(np.float32)
for k in inputs:
inputs[k] = inputs[k].reshape((inputs[k].shape[0], 1))
我们准备好运行onnxruntime了。
sess = rt.InferenceSession("pipeline_titanic.onnx", providers=["CPUExecutionProvider"])
pred_onx = sess.run(None, inputs)
print("predict", pred_onx[0][:5])
print("predict_proba", pred_onx[1][:1])
predict [0 0 0 0 1]
predict_proba [{0: 0.9543983340263367, 1: 0.04560166597366333}]
计算中间输出¶
遗憾的是，目前实际上没有办法要求onnxruntime检索中间节点的输出。我们需要在将ONNX提供给onnxruntime之前对其进行修改。首先让我们看看中间输出列表。
model_onnx = load_onnx_model("pipeline_titanic.onnx")
for out in enumerate_model_node_outputs(model_onnx):
print(out)
merged_columns
embarkedout
sexout
pclassout
concat_result
variable
variable2
variable1
transformed_column
label
probabilities
output_label
output_probability
由于ONNX比原始的scikit-learn流水线拥有更多的算子，因此不容易区分哪个是哪个。图位于显示ONNX图，它有助于找到数值和文本流水线的输出：*variable1*，*variable2*。我们首先查看数值流水线。
num_onnx = select_model_inputs_outputs(model_onnx, "variable1")
save_onnx_model(num_onnx, "pipeline_titanic_numerical.onnx")
b'\x08\x07\x12\x08skl2onnx\x1a\x061.18.0"\x07ai.onnx(\x002\x00:\xcd\x03\n:\n\x03age\n\x04fare\x12\x0emerged_columns\x1a\x06Concat"\x06Concat*\x0b\n\x04axis\x18\x01\xa0\x01\x02:\x00\n}\n\x0emerged_columns\x12\x08variable\x1a\x07Imputer"\x07Imputer*#\n\x14imputed_value_floats=\x00\x00\xe0A=2UgA\xa0\x01\x06*\x1e\n\x14replaced_value_float\x15\x00\x00\xc0\x7f\xa0\x01\x01:\nai.onnx.ml\n^\n\x08variable\x12\tvariable1\x1a\x06Scaler"\x06Scaler*\x15\n\x06offset=f\x13\xecA=\xc3\xad\x02B\xa0\x01\x06*\x14\n\x05scale=h7\x9f==\x1a\xbc\xa7<\xa0\x01\x06:\nai.onnx.ml\x12\x10pipeline_titanic*\x1f\x08\x02\x10\x07:\x0b\xff\xff\xff\xff\xff\xff\xff\xff\xff\x01\tB\x0cshape_tensorZ\x16\n\x06pclass\x12\x0c\n\n\x08\x08\x12\x06\n\x00\n\x02\x08\x01Z\x13\n\x03sex\x12\x0c\n\n\x08\x08\x12\x06\n\x00\n\x02\x08\x01Z\x13\n\x03age\x12\x0c\n\n\x08\x01\x12\x06\n\x00\n\x02\x08\x01Z\x14\n\x04fare\x12\x0c\n\n\x08\x01\x12\x06\n\x00\n\x02\x08\x01Z\x18\n\x08embarked\x12\x0c\n\n\x08\x08\x12\x06\n\x00\n\x02\x08\x01b\x0b\n\tvariable1B\x04\n\x00\x10\x0bB\x0e\n\nai.onnx.ml\x10\x01'
让我们计算数值特征。
sess = rt.InferenceSession(
"pipeline_titanic_numerical.onnx", providers=["CPUExecutionProvider"]
)
numX = sess.run(None, inputs)
print("numerical features", numX[0][:1])
numerical features [[ 1.6707252 -0.52448833]]
我们对文本特征做同样的操作。
print(model_onnx)
text_onnx = select_model_inputs_outputs(model_onnx, "variable2")
save_onnx_model(text_onnx, "pipeline_titanic_textual.onnx")
sess = rt.InferenceSession(
"pipeline_titanic_textual.onnx", providers=["CPUExecutionProvider"]
)
numT = sess.run(None, inputs)
print("textual features", numT[0][:1])
ir_version: 7
producer_name: "skl2onnx"
producer_version: "1.18.0"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
node {
input: "age"
input: "fare"
output: "merged_columns"
name: "Concat"
op_type: "Concat"
attribute {
name: "axis"
i: 1
type: INT
}
domain: ""
}
node {
input: "embarked"
output: "embarkedout"
name: "OneHotEncoder"
op_type: "OneHotEncoder"
attribute {
name: "cats_strings"
strings: "C"
strings: "Q"
strings: "S"
strings: "missing"
type: STRINGS
}
attribute {
name: "zeros"
i: 1
type: INT
}
domain: "ai.onnx.ml"
}
node {
input: "sex"
output: "sexout"
name: "OneHotEncoder1"
op_type: "OneHotEncoder"
attribute {
name: "cats_strings"
strings: "female"
strings: "male"
type: STRINGS
}
attribute {
name: "zeros"
i: 1
type: INT
}
domain: "ai.onnx.ml"
}
node {
input: "pclass"
output: "pclassout"
name: "OneHotEncoder2"
op_type: "OneHotEncoder"
attribute {
name: "cats_strings"
strings: "1"
strings: "2"
strings: "3"
type: STRINGS
}
attribute {
name: "zeros"
i: 1
type: INT
}
domain: "ai.onnx.ml"
}
node {
input: "embarkedout"
input: "sexout"
input: "pclassout"
output: "concat_result"
name: "Concat1"
op_type: "Concat"
attribute {
name: "axis"
i: -1
type: INT
}
domain: ""
}
node {
input: "merged_columns"
output: "variable"
name: "Imputer"
op_type: "Imputer"
attribute {
name: "imputed_value_floats"
floats: 28
floats: 14.4583
type: FLOATS
}
attribute {
name: "replaced_value_float"
f: nan
type: FLOAT
}
domain: "ai.onnx.ml"
}
node {
input: "concat_result"
input: "shape_tensor"
output: "variable2"
name: "Reshape"
op_type: "Reshape"
domain: ""
}
node {
input: "variable"
output: "variable1"
name: "Scaler"
op_type: "Scaler"
attribute {
name: "offset"
floats: 29.5094719
floats: 32.6696892
type: FLOATS
}
attribute {
name: "scale"
floats: 0.0777424
floats: 0.020475436
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "variable1"
input: "variable2"
output: "transformed_column"
name: "Concat2"
op_type: "Concat"
attribute {
name: "axis"
i: 1
type: INT
}
domain: ""
}
node {
input: "transformed_column"
output: "label"
output: "probabilities"
name: "LinearClassifier"
op_type: "LinearClassifier"
attribute {
name: "classlabels_ints"
ints: 0
ints: 1
type: INTS
}
attribute {
name: "coefficients"
floats: 0.414287567
floats: 0.0486155599
floats: -0.254193634
floats: 0.0872347131
floats: 0.349803418
floats: -0.259518
floats: -1.27854633
floats: 1.20187283
floats: -1.06555367
floats: -0.00171175227
floats: 0.990591884
floats: -0.414287567
floats: -0.0486155599
floats: 0.254193634
floats: -0.0872347131
floats: -0.349803418
floats: 0.259518
floats: 1.27854633
floats: -1.20187283
floats: 1.06555367
floats: 0.00171175227
floats: -0.990591884
type: FLOATS
}
attribute {
name: "intercepts"
floats: -0.1677939
floats: 0.1677939
type: FLOATS
}
attribute {
name: "multi_class"
i: 0
type: INT
}
attribute {
name: "post_transform"
s: "LOGISTIC"
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
type: INTS
}
domain: "ai.onnx.ml"
}
name: "pipeline_titanic"
initializer {
dims: 2
data_type: 7
int64_data: -1
int64_data: 9
name: "shape_tensor"
}
input {
name: "pclass"
type {
tensor_type {
elem_type: 8
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
input {
name: "sex"
type {
tensor_type {
elem_type: 8
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
input {
name: "age"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
input {
name: "fare"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
input {
name: "embarked"
type {
tensor_type {
elem_type: 8
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
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: 11
}
opset_import {
domain: "ai.onnx.ml"
version: 1
}
textual features [[0. 0. 1. 0. 0. 1. 0. 0. 1.]]
显示子ONNX图¶
最后，我们看看两个子图。首先是数值流水线。
pydot_graph = GetPydotGraph(
num_onnx.graph,
name=num_onnx.graph.name,
rankdir="TB",
node_producer=GetOpNodeProducer(
"docstring", color="yellow", fillcolor="yellow", style="filled"
),
)
pydot_graph.write_dot("pipeline_titanic_num.dot")
os.system("dot -O -Gdpi=300 -Tpng pipeline_titanic_num.dot")
image = plt.imread("pipeline_titanic_num.dot.png")
fig, ax = plt.subplots(figsize=(40, 20))
ax.imshow(image)
ax.axis("off")
(np.float64(-0.5), np.float64(1229.5), np.float64(2558.5), np.float64(-0.5))
然后是文本流水线。
pydot_graph = GetPydotGraph(
text_onnx.graph,
name=text_onnx.graph.name,
rankdir="TB",
node_producer=GetOpNodeProducer(
"docstring", color="yellow", fillcolor="yellow", style="filled"
),
)
pydot_graph.write_dot("pipeline_titanic_text.dot")
os.system("dot -O -Gdpi=300 -Tpng pipeline_titanic_text.dot")
image = plt.imread("pipeline_titanic_text.dot.png")
fig, ax = plt.subplots(figsize=(40, 20))
ax.imshow(image)
ax.axis("off")
(np.float64(-0.5), np.float64(5630.5), np.float64(2735.5), np.float64(-0.5))
本示例使用的版本
print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", rt.__version__)
print("skl2onnx: ", skl2onnx.__version__)
numpy: 2.2.0
scikit-learn: 1.6.0
onnx: 1.18.0
onnxruntime: 1.21.0+cu126
skl2onnx: 1.18.0
**脚本总运行时间：** (0 分钟 2.980 秒)