sklearn-json
Export scikit-learn model files to JSON for sharing or deploying predictive models with peace of mind.
Why sklearn-json?
Other methods for exporting scikit-learn models require Pickle or Joblib (based on Pickle). Serializing model files with Pickle provide a simple attack vector for malicious users-- they give an attacker the ability to execute arbitrary code wherever the file is deserialized. (For an example see: https://www.smartfile.com/blog/python-pickle-security-problems-and-solutions/).
sklearn-json is a safe and transparent solution for exporting scikit-learn model files.
Safe
Export model files to 100% JSON which cannot execute code on deserialization.
Transparent
Model files are serialized in JSON (i.e., not binary), so you have the ability to see exactly what's inside.
Getting Started
sklearn-json makes exporting model files to JSON simple.
Install
pip install sklearn-json
Example Usage
import sklearn_json as skljson
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=0).fit(X, y)
skljson.to_json(model, file_name)
deserialized_model = skljson.from_json(file_name)
deserialized_model.predict(X)
Features
The list of supported models is rapidly growing. If you have a request for a model or feature, please reach out to support@mlrequest.com.
sklearn-json requires scikit-learn >= 0.21.3.
Supported scikit-learn Models
-
Classification
sklearn.linear_model.LogisticRegressionsklearn.linear_model.Perceptronsklearn.discriminant_analysis.LinearDiscriminantAnalysissklearn.discriminant_analysis.QuadraticDiscriminantAnalysissklearn.svm.SVCsklearn.naive_bayes.GaussianNBsklearn.naive_bayes.MultinomialNBsklearn.naive_bayes.ComplementNBsklearn.naive_bayes.BernoulliNBsklearn.tree.DecisionTreeClassifiersklearn.ensemble.RandomForestClassifiersklearn.ensemble.GradientBoostingClassifiersklearn.neural_network.MLPClassifier
-
Regression
sklearn.linear_model.LinearRegressionsklearn.linear_model.Ridgesklearn.linear_model.Lassosklearn.svm.SVRsklearn.tree.DecisionTreeRegressorsklearn.ensemble.RandomForestRegressorsklearn.ensemble.GradientBoostingRegressorsklearn.neural_network.MLPRegressor
Metadata
Release files for sklearn-json 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| sklearn-json-0.1.0.tar.gz | 9.5 kB | Without provenance | Details for sklearn-json-0.1.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| sklearn_json-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for sklearn_json-0.1.0-py3-none-any.whl |
Total release size: 22.5 kB
Release files / sklearn-json-0.1.0.tar.gz
| Download URL | sklearn-json-0.1.0.tar.gz |
|---|---|
| Size | 9.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
9c7ab24064705adfa9fdea2b83645545559d58607501fd2328c8fae7f138ee25
|
|
BLAKE2b-256 checksum How to use checksums |
95eb2097ec853072efec5a52a3ebdaaf70f3fae5d6df3c4dc050556397734509
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/2.0.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.28.1 CPython/3.7.1
|
Release files / sklearn_json-0.1.0-py3-none-any.whl
| Download URL | sklearn_json-0.1.0-py3-none-any.whl |
|---|---|
| Size | 13.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
f676f388e5a935b419c7674279b6415de8a6416967a7279a3a7f44a86544ac2f
|
|
BLAKE2b-256 checksum How to use checksums |
f7715fd22261cd303bf0c67f3ebdbb7bca8810f9c3345191e6686464dc5cc22e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/2.0.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.28.1 CPython/3.7.1
|
Security for sklearn-json 0.1.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.