skjson 0.1.1

Export scikit-learn models to JSON for cross-language inference

skjson

Export scikit-learn models to JSON for cross-language inference.

Installation

pip install skjson

Quick Start

Check out the demo notebook for an example training a Random Forest on the Iris dataset:

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import skjson

# Load the Iris dataset and split
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a Random Forest classifier
clf = RandomForestClassifier(random_state=42)
clf.fit(X_train, y_train)

# Export the trained model to JSON
skjson.save(clf, 'demo.json')

# Load and reuse the model
predictor = skjson.load("demo.json")

Support models for conversion

Linear Models (sklearn.linear_model)

• LinearRegression, Ridge, Lasso, ElasticNet
• LogisticRegression

Tree-based Models (sklearn.tree, sklearn.ensemble)

• DecisionTreeClassifier, DecisionTreeRegressor
• RandomForestClassifier, RandomForestRegressor
• GradientBoostingClassifier, GradientBoostingRegressor

Support Vector Machines (sklearn.svm)

• SVC, SVR, LinearSVC

Neighbors (sklearn.neighbors)

• KNeighborsClassifier, KNeighborsRegressor

Naive Bayes (sklearn.naive_bayes)

• GaussianNB

Preprocessing (sklearn.preprocessing)

• StandardScaler, MinMaxScaler, LabelEncoder

Future Directions

• Support for scikit-learn Pipeline objects and feature unions.
• npm package for inference using our json models
• Support for sklearn.neural_network models

Note

• This project built with Google Gemini and Claude Opus.
• If you wish to contribute, please contact me.