This project aims to used text exported ML models generated by sci-kit learn and make them usable in Java.
- The tree.DecisionTreeClassifier is supported
- Supports
predict(), - Supports
predict_proba()whenexport_text()configured withshow_weights=True
- Supports
- The tree.RandomForestClassifier is supported
- Supports
predict(), - Supports
predict_proba()whenexport_text()configured withshow_weights=True
- Supports
<dependency>
<groupId>rocks.vilaverde</groupId>
<artifactId>scikit-learn-2-java</artifactId>
<version>1.1.0</version>
</dependency>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.
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<Integer> 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());
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:
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<Double> decisionTree = RandomForestClassifier.parse(tree,
PredictionFactory.DOUBLE);
Testing was done using models exported using sci-kit learn version 1.1.3, but should work with newer versions of sci-kit learn.
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