注意
转到末尾下载完整示例代码。
一个模型，多种可能的带选项转换¶
转换模型的方式并非只有一种。新版本的 ONNX 中可能添加了新的操作符，这会加快转换后模型的速度。合理的选择是使用这个新的操作符，但这取决于相关的运行时是否已经实现它。如果两个不同的用户对同一个模型需要两种不同的转换方式怎么办？让我们看看如何做到这一点。
选项 zipmap¶
默认情况下，每个分类器都被转换成一个 ONNX 图，输出两个结果：预测标签和每个标签的预测概率。默认情况下，标签是整数，概率存储在字典中。这就是以下图末尾添加的 ZipMap 操作符的目的。
graph ONNX(LogisticRegression) (
%X[FLOAT, ?x4]
) {
%label, %probability_tensor = LinearClassifier[classlabels_ints = [0, 1, 2], coefficients = [-0.374590873718262, 0.882017612457275, -2.25903177261353, -0.96484386920929, 0.463038802146912, -0.698963463306427, -0.0836651995778084, -0.888288736343384, -0.0884479135274887, -0.18305416405201, 2.34269690513611, 1.85313260555267], intercepts = [8.58371162414551, 2.95640826225281, -11.5401201248169], multi_class = 1, post_transform = 'SOFTMAX'](%X)
%output_label = Cast[to = 7](%label)
%probabilities = Normalizer[norm = 'L1'](%probability_tensor)
%output_probability = ZipMap[classlabels_int64s = [0, 1, 2]](%probabilities)
return %output_label, %output_probability
}
这个操作符效率不高，因为它将每个概率和标签复制到不同的容器中。对于小型分类器来说，这个时间通常很明显。因此，移除它是合理的。
graph ONNX(LogisticRegression) (
%X[FLOAT, ?x4]
) {
%label, %probability_tensor = LinearClassifier[classlabels_ints = [0, 1, 2], coefficients = [-0.374590873718262, 0.882017612457275, -2.25903177261353, -0.96484386920929, 0.463038802146912, -0.698963463306427, -0.0836651995778084, -0.888288736343384, -0.0884479135274887, -0.18305416405201, 2.34269690513611, 1.85313260555267], intercepts = [8.58371162414551, 2.95640826225281, -11.5401201248169], multi_class = 1, post_transform = 'SOFTMAX'](%X)
%probabilities = Normalizer[norm = 'L1'](%probability_tensor)
return %label, %probabilities
}
图中可能包含多个分类器，重要的是能够指定哪个分类器应该保留其 ZipMap，哪个不保留。因此，可以通过 ID 来指定选项。
from pprint import pformat
import numpy
from onnx.reference import ReferenceEvaluator
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from skl2onnx.common._registration import _converter_pool
from skl2onnx import to_onnx
from onnxruntime import InferenceSession
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
clr = LogisticRegression()
clr.fit(X_train, y_train)
model_def = to_onnx(
clr, X_train.astype(numpy.float32), options={id(clr): {"zipmap": False}}
)
oinf = ReferenceEvaluator(model_def)
print(oinf)
ReferenceEvaluator(X) -> label, probabilities
使用函数 id 有一个缺点：它不可序列化 (not pickable)。最好还是使用字符串。
model_def = to_onnx(clr, X_train.astype(numpy.float32), options={"zipmap": False})
oinf = ReferenceEvaluator(model_def)
print(oinf)
ReferenceEvaluator(X) -> label, probabilities
流水线中的选项¶
在流水线中，sklearn-onnx 使用相同的命名约定。
pipe = Pipeline([("norm", MinMaxScaler()), ("clr", LogisticRegression())])
pipe.fit(X_train, y_train)
model_def = to_onnx(pipe, X_train.astype(numpy.float32), options={"clr__zipmap": False})
oinf = ReferenceEvaluator(model_def)
print(oinf)
ReferenceEvaluator(X) -> label, probabilities
选项 raw_scores¶
默认情况下，每个分类器都被转换成一个返回概率的图。但是许多模型计算未缩放的 raw_scores。首先，使用概率：
pipe = Pipeline([("norm", MinMaxScaler()), ("clr", LogisticRegression())])
pipe.fit(X_train, y_train)
model_def = to_onnx(
pipe, X_train.astype(numpy.float32), options={id(pipe): {"zipmap": False}}
)
oinf = ReferenceEvaluator(model_def)
print(oinf.run(None, {"X": X.astype(numpy.float32)[:5]}))
[array([0, 0, 0, 0, 0]), array([[0.8826898 , 0.10948468, 0.00782558],
[0.7944286 , 0.19729899, 0.00827242],
[0.8555814 , 0.13791925, 0.00649932],
[0.82628906, 0.16633531, 0.00737559],
[0.9005094 , 0.09238414, 0.00710642]], dtype=float32)]
然后使用原始分数：
model_def = to_onnx(
pipe,
X_train.astype(numpy.float32),
options={id(pipe): {"raw_scores": True, "zipmap": False}},
)
oinf = ReferenceEvaluator(model_def)
print(oinf.run(None, {"X": X.astype(numpy.float32)[:5]}))
[array([0, 0, 0, 0, 0]), array([[0.8826898 , 0.10948468, 0.00782558],
[0.7944286 , 0.19729899, 0.00827242],
[0.8555814 , 0.13791925, 0.00649932],
[0.82628906, 0.16633531, 0.00737559],
[0.9005094 , 0.09238414, 0.00710642]], dtype=float32)]
看起来没有起作用……我们需要指定这个选项应用于流水线的特定部分，而不是整个流水线。
model_def = to_onnx(
pipe,
X_train.astype(numpy.float32),
options={id(pipe.steps[1][1]): {"raw_scores": True, "zipmap": False}},
)
oinf = ReferenceEvaluator(model_def)
print(oinf.run(None, {"X": X.astype(numpy.float32)[:5]}))
[array([0, 0, 0, 0, 0]), array([[ 2.2709217 , 0.18373239, -2.454654 ],
[ 1.9858665 , 0.59296364, -2.57883 ],
[ 2.2350655 , 0.40995252, -2.6450179 ],
[ 2.1072361 , 0.5042972 , -2.6115332 ],
[ 2.3729892 , 0.09598386, -2.468973 ]], dtype=float32)]
有负值。这下对了。字符串仍然更容易使用。
model_def = to_onnx(
pipe,
X_train.astype(numpy.float32),
options={"clr__raw_scores": True, "clr__zipmap": False},
)
oinf = ReferenceEvaluator(model_def)
print(oinf.run(None, {"X": X.astype(numpy.float32)[:5]}))
[array([0, 0, 0, 0, 0]), array([[ 2.2709217 , 0.18373239, -2.454654 ],
[ 1.9858665 , 0.59296364, -2.57883 ],
[ 2.2350655 , 0.40995252, -2.6450179 ],
[ 2.1072361 , 0.5042972 , -2.6115332 ],
[ 2.3729892 , 0.09598386, -2.468973 ]], dtype=float32)]
负数。我们仍然得到原始分数。
选项 decision_path¶
scikit-learn 实现了一个函数来获取决策路径。可以通过选项 decision_path 启用它。
clrrf = RandomForestClassifier(n_estimators=2, max_depth=2)
clrrf.fit(X_train, y_train)
clrrf.predict(X_test[:2])
paths, n_nodes_ptr = clrrf.decision_path(X_test[:2])
print(paths.todense())
model_def = to_onnx(
clrrf,
X_train.astype(numpy.float32),
options={id(clrrf): {"decision_path": True, "zipmap": False}},
)
sess = InferenceSession(
model_def.SerializeToString(), providers=["CPUExecutionProvider"]
)
[[1 0 0 0 1 0 1 1 0 1 0 1]
[1 0 0 0 1 0 1 1 0 1 0 1]]
模型产生 3 个输出。
print([o.name for o in sess.get_outputs()])
['label', 'probabilities', 'decision_path']
让我们显示最后一个。
res = sess.run(None, {"X": X_test[:2].astype(numpy.float32)})
print(res[-1])
[['1000101' '10101']
['1000101' '10101']]
可用选项列表¶
选项在每个转换器中注册，以便在运行转换时检测任何支持的选项。
all_opts = set()
for k, v in sorted(_converter_pool.items()):
opts = v.get_allowed_options()
if not isinstance(opts, dict):
continue
name = k.replace("Sklearn", "")
print("%s%s %r" % (name, " " * (30 - len(name)), opts))
for o in opts:
all_opts.add(o)
print("all options:", pformat(list(sorted(all_opts))))
Skl2onnxTraceableCountVectorizer {'tokenexp': None, 'separators': None, 'nan': [True, False], 'keep_empty_string': [True, False], 'locale': None}
Skl2onnxTraceableTfidfVectorizer {'tokenexp': None, 'separators': None, 'nan': [True, False], 'keep_empty_string': [True, False], 'locale': None}
AdaBoostClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
BaggingClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
BayesianGaussianMixture {'score_samples': [True, False]}
BayesianRidge {'return_std': [True, False]}
BernoulliNB {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
CalibratedClassifierCV {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
CategoricalNB {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
ComplementNB {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
CountVectorizer {'tokenexp': None, 'separators': None, 'nan': [True, False], 'keep_empty_string': [True, False], 'locale': None}
DecisionTreeClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'decision_path': [True, False], 'decision_leaf': [True, False]}
DecisionTreeRegressor {'decision_path': [True, False], 'decision_leaf': [True, False]}
ExtraTreeClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'decision_path': [True, False], 'decision_leaf': [True, False]}
ExtraTreeRegressor {'decision_path': [True, False], 'decision_leaf': [True, False]}
ExtraTreesClassifier {'zipmap': [True, False, 'columns'], 'raw_scores': [True, False], 'nocl': [True, False], 'output_class_labels': [False, True], 'decision_path': [True, False], 'decision_leaf': [True, False]}
ExtraTreesRegressor {'decision_path': [True, False], 'decision_leaf': [True, False]}
FeatureHasher {'separator': None}
GaussianMixture {'score_samples': [True, False]}
GaussianNB {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
GaussianProcessClassifier {'optim': [None, 'cdist'], 'nocl': [False, True], 'output_class_labels': [False, True], 'zipmap': [False, True]}
GaussianProcessRegressor {'return_cov': [False, True], 'return_std': [False, True], 'optim': [None, 'cdist']}
GradientBoostingClassifier {'zipmap': [True, False, 'columns'], 'raw_scores': [True, False], 'output_class_labels': [False, True], 'nocl': [True, False]}
HistGradientBoostingClassifier {'zipmap': [True, False, 'columns'], 'raw_scores': [True, False], 'output_class_labels': [False, True], 'nocl': [True, False]}
HistGradientBoostingRegressor {'raw_scores': [True, False]}
IsolationForest {'score_samples': [True, False]}
KMeans {'gemm': [True, False]}
KNNImputer {'optim': [None, 'cdist']}
KNeighborsClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'raw_scores': [True, False], 'output_class_labels': [False, True], 'optim': [None, 'cdist']}
KNeighborsRegressor {'optim': [None, 'cdist']}
KNeighborsTransformer {'optim': [None, 'cdist']}
KernelPCA {'optim': [None, 'cdist']}
LinearClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
LinearSVC {'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
LocalOutlierFactor {'score_samples': [True, False], 'optim': [None, 'cdist']}
MLPClassifier {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
MaxAbsScaler {'div': ['std', 'div', 'div_cast']}
MiniBatchKMeans {'gemm': [True, False]}
MultiOutputClassifier {'nocl': [False, True], 'output_class_labels': [False, True], 'zipmap': [False, True]}
MultinomialNB {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
NearestNeighbors {'optim': [None, 'cdist']}
OneVsOneClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True]}
OneVsRestClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
Pipeline {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
QuadraticDiscriminantAnalysis {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True]}
RadiusNeighborsClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'raw_scores': [True, False], 'output_class_labels': [False, True], 'optim': [None, 'cdist']}
RadiusNeighborsRegressor {'optim': [None, 'cdist']}
RandomForestClassifier {'zipmap': [True, False, 'columns'], 'raw_scores': [True, False], 'nocl': [True, False], 'output_class_labels': [False, True], 'decision_path': [True, False], 'decision_leaf': [True, False]}
RandomForestRegressor {'decision_path': [True, False], 'decision_leaf': [True, False]}
RobustScaler {'div': ['std', 'div', 'div_cast']}
SGDClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
SVC {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
Scaler {'div': ['std', 'div', 'div_cast']}
StackingClassifier {'zipmap': [True, False, 'columns'], 'nocl': [True, False], 'output_class_labels': [False, True], 'raw_scores': [True, False]}
TfidfTransformer {'nan': [True, False]}
TfidfVectorizer {'tokenexp': None, 'separators': None, 'nan': [True, False], 'keep_empty_string': [True, False], 'locale': None}
TunedThresholdClassifierCV {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
VotingClassifier {'zipmap': [True, False, 'columns'], 'output_class_labels': [False, True], 'nocl': [True, False]}
_ConstantPredictor {'zipmap': [True, False, 'columns'], 'nocl': [True, False]}
all options: ['decision_leaf',
'decision_path',
'div',
'gemm',
'keep_empty_string',
'locale',
'nan',
'nocl',
'optim',
'output_class_labels',
'raw_scores',
'return_cov',
'return_std',
'score_samples',
'separator',
'separators',
'tokenexp',
'zipmap']
脚本总运行时间： (0 分 0.109 秒)