https://github.com/dvilaverde/sklearn2java

A parser for scikit-learn exported text models to execute in the Java runtime.
https://github.com/dvilaverde/sklearn2java

decision-tree decisiontreeclassifier java machine-learning ml random-forest randomforestclassifier sci-kit scikit scikit-learn scikitlearn-machine-learning sklearn

Last synced: 3 days ago
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A parser for scikit-learn exported text models to execute in the Java runtime.

• Host: GitHub
• URL: https://github.com/dvilaverde/sklearn2java
• Owner: dvilaverde
• License: apache-2.0
• Created: 2022-12-11T18:52:02.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-04-28T18:13:58.000Z (7 months ago)
• Last Synced: 2024-04-28T19:25:35.195Z (7 months ago)
• Topics: decision-tree, decisiontreeclassifier, java, machine-learning, ml, random-forest, randomforestclassifier, sci-kit, scikit, scikit-learn, scikitlearn-machine-learning, sklearn
• Language: Java
• Homepage:
• Size: 98.6 KB
• Stars: 3
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SkLearn2Java

This project aims to used text exported ML models generated by sci-kit learn and make them usable in Java.

[![javadoc](https://javadoc.io/badge2/rocks.vilaverde/scikit-learn-2-java/javadoc.svg)](https://javadoc.io/doc/rocks.vilaverde/scikit-learn-2-java)

## Support

* The tree.DecisionTreeClassifier is supported

  * Supports `predict()`,

  * Supports `predict_proba()` when `export_text()` configured with `show_weights=True`

* The tree.RandomForestClassifier is supported

  * Supports `predict()`,

  * Supports `predict_proba()` when `export_text()` configured with `show_weights=True`

## Installing

### Importing Maven Dependency

```xml

  rocks.vilaverde

  scikit-learn-2-java

  1.1.0

```

## DecisionTreeClassifier

As an example, a DecisionTreeClassifier model trained on the Iris dataset and exported using `sklearn.tree`

`export_text()` as shown below:

```

>>> from sklearn.datasets import load_iris

>>> from sklearn.tree import DecisionTreeClassifier

>>> from sklearn.tree import export_text

>>> iris = load_iris()

>>> X = iris['data']

>>> y = iris['target']

>>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)

>>> decision_tree = decision_tree.fit(X, y)

>>> r = export_text(decision_tree, feature_names=iris['feature_names'], show_weights=True, max_depth=sys.maxsize)

>>> print(r)

|--- petal width (cm) <= 0.80

|   |--- class: 0

|--- petal width (cm) >  0.80

|   |--- petal width (cm) <= 1.75

|   |   |--- class: 1

|   |--- petal width (cm) >  1.75

|   |   |--- class: 2

```

The exported text can then be executed in Java. Note that when calling `export_text` it is 

recommended that `max_depth` be set to `sys.maxsize` so that the tree isn't truncated.

### Java Example

In this example the iris model exported using `export_text` is parsed, features are created as a Java Map

and the decision tree is asked to predict the class.

```

    Reader tree = getTrainedModel("iris.model");

    final Classifier decisionTree = DecisionTreeClassifier.parse(tree,

                PredictionFactory.INTEGER);

    Features features = Features.of("sepal length (cm)",

                "sepal width (cm)",

                "petal length (cm)",

                "petal width (cm)");

    FeatureVector fv = features.newSample();

    fv.add(0, 3.0).add(1, 5.0).add(2, 4.0).add(3, 2.0);

    

    Integer prediction = decisionTree.predict(fv);

    System.out.println(prediction.toString());

```

## RandomForestClassifier

To use a RandomForestClassifier that has been trained on the Iris dataset, each of the `estimators`  

in the classifiers need to be and exported using `from sklearn.tree export export_text` as shown below:

```

>>> from sklearn import datasets

>>> from sklearn import tree

>>> from sklearn.ensemble import RandomForestClassifier

>>> 

>>> import os

>>> 

>>> iris = datasets.load_iris()

>>> X = iris.data

>>> y = iris.target

>>> 

>>> clf = RandomForestClassifier(n_estimators = 50, n_jobs=8)

>>> model = clf.fit(X, y)

>>> 

>>> for i, t in enumerate(clf.estimators_):

>>>     with open(os.path.join('/tmp/estimators', "iris-" + str(i) + ".txt"), "w") as file1:

>>>         text_representation = tree.export_text(t, feature_names=iris.feature_names, show_weights=True, decimals=4, max_depth=sys.maxsize)

>>>         file1.write(text_representation)

```

Once all the estimators are exported into `/tmp/estimators`, you can create a TAR archive, for example:

```bash

cd /tmp/estimators

tar -czvf /tmp/iris.tgz .

```

Then you can use the RandomForestClassifier class to parse the TAR archive.

```

    import org.apache.commons.compress.archivers.tar.TarArchiveInputStream;

    ...

    

    TarArchiveInputStream tree = getArchive("iris.tgz");

    final Classifier decisionTree = RandomForestClassifier.parse(tree,

                PredictionFactory.DOUBLE);

```

## Testing

Testing was done using models exported using sci-kit learn version 1.1.3, but should 

work with newer versions of sci-kit learn.