sklearn-onnx | Convert scikit-learn models and pipelines to ONNX | Machine Learning library
kandi X-RAY | sklearn-onnx Summary
kandi X-RAY | sklearn-onnx Summary
sklearn-onnx is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Keras applications. sklearn-onnx has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install sklearn-onnx' or download it from GitHub, PyPI.
sklearn-onnx converts scikit-learn models to ONNX. Once in the ONNX format, you can use tools like ONNX Runtime for high performance scoring. All converters are tested with onnxruntime.
sklearn-onnx converts scikit-learn models to ONNX. Once in the ONNX format, you can use tools like ONNX Runtime for high performance scoring. All converters are tested with onnxruntime.
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sklearn-onnx has a low active ecosystem.
It has 430 star(s) with 86 fork(s). There are 17 watchers for this library.
It had no major release in the last 12 months.
There are 68 open issues and 293 have been closed. On average issues are closed in 292 days. There are 3 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of sklearn-onnx is 1.14.1
Quality
sklearn-onnx has 0 bugs and 0 code smells.
Security
sklearn-onnx has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
sklearn-onnx code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
sklearn-onnx is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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sklearn-onnx releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
sklearn-onnx saves you 18708 person hours of effort in developing the same functionality from scratch.
It has 36976 lines of code, 1468 functions and 281 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed sklearn-onnx and discovered the below as its top functions. This is intended to give you an instant insight into sklearn-onnx implemented functionality, and help decide if they suit your requirements.
- Convert a sklearn TextVectorizer operator
- Return a unique operator name
- Returns a unique variable name
- Generate a unique name for the given seed
- Convert a sklearn - naive sklearn naive
- Define the Bernstein joint log - likelihood
- Guess the numpy type for the given data_type
- Construct the joint log likelihoods for the given model
- Convert a Gaussian Process classifier
- Convert a scaler operator into a scaler operator
- Convert a sklearn svm classifier operator into an SVMClassifier
- Convert the operator to onnx
- Convert a sklearn decision tree classifier
- Convert a quadratic discriminator
- Convert an operator to onnx classifier
- Convert an operator to onnx
- Map operator to onnx operator
- Convert a GaussianProcessRegressor
- Convert an operator classifier to onnx
- Convert a sklearn random forest regressor
- Convert a OneHotEncoder operator
- Build the name map for sklearn operator names
- Convert a sklearn_gaussian_matrix
- Convert a sklearn sklearn model
- Convert a sklearn random forest classifier
- Convert a sklearnisolation forest
Get all kandi verified functions for this library.
sklearn-onnx Key Features
No Key Features are available at this moment for sklearn-onnx.
sklearn-onnx Examples and Code Snippets
No Code Snippets are available at this moment for sklearn-onnx.
Community Discussions
Trending Discussions on sklearn-onnx
QUESTION
Is it possible to get tree decision_path from calls to sklearn models saved with skl2onnx?
Asked 2020-Feb-05 at 19:52
For example following: https://github.com/onnx/sklearn-onnx/blob/master/docs/tutorial.rst
But with
...ANSWER
Answered 2020-Feb-05 at 19:52This is unfortunately not possible -- the skl2onnx converter does not expose the decision path in a converted ONNX model. In the case of SciKitLearn's Regressors, we only expose the predicted score in the ONNX model. In Classifiers, we expose the predicted class and the probability / decision function scores.
There is also no way to take a converted ONNX model and load it back into SciKitLearn.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install sklearn-onnx
You can install from PyPi:. Or you can install from the source with the latest changes.
Support
Full documentation including tutorials is available at https://onnx.ai/sklearn-onnx/. Supported scikit-learn Models Last supported opset is 15. You may also find answers in existing issues or submit a new one.
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