How can I call scikit-learn classifiers from Java?
Question:
I have a classifier that I trained using Python’s scikit-learn. How can I use the classifier from a Java program? Can I use Jython? Is there some way to save the classifier in Python and load it in Java? Is there some other way to use it?
Answers:
You cannot use jython as scikit-learn heavily relies on numpy and scipy that have many compiled C and Fortran extensions hence cannot work in jython.
The easiest ways to use scikit-learn in a java environment would be to:
-
expose the classifier as a HTTP / Json service, for instance using a microframework such as flask or bottle or cornice and call it from java using an HTTP client library
-
write a commandline wrapper application in python that reads data on stdin and output predictions on stdout using some format such as CSV or JSON (or some lower level binary representation) and call the python program from java for instance using Apache Commons Exec.
-
make the python program output the raw numerical parameters learnt at fit time (typically as an array of floating point values) and reimplement the predict function in java (this is typically easy for predictive linear models where the prediction is often just a thresholded dot product).
The last approach will be a lot more work if you need to re-implement feature extraction in Java as well.
Finally you can use a Java library such as Weka or Mahout that implement the algorithms you need instead of trying to use scikit-learn from Java.
There is JPMML project for this purpose.
First, you can serialize scikit-learn model to PMML (which is XML internally) using sklearn2pmml library directly from python or dump it in python first and convert using jpmml-sklearn in java or from a command line provided by this library. Next, you can load pmml file, deserialize and execute loaded model using jpmml-evaluator in your Java code.
This way works with not all scikit-learn models, but with many of them.
Here is some code for the JPMML solution:
–PYTHON PART–
# helper function to determine the string columns which have to be one-hot-encoded in order to apply an estimator.
def determine_categorical_columns(df):
categorical_columns = []
x = 0
for col in df.dtypes:
if col == 'object':
val = df[df.columns[x]].iloc[0]
if not isinstance(val,Decimal):
categorical_columns.append(df.columns[x])
x += 1
return categorical_columns
categorical_columns = determine_categorical_columns(df)
other_columns = list(set(df.columns).difference(categorical_columns))
#construction of transformators for our example
labelBinarizers = [(d, LabelBinarizer()) for d in categorical_columns]
nones = [(d, None) for d in other_columns]
transformators = labelBinarizers+nones
mapper = DataFrameMapper(transformators,df_out=True)
gbc = GradientBoostingClassifier()
#construction of the pipeline
lm = PMMLPipeline([
("mapper", mapper),
("estimator", gbc)
])
–JAVA PART —
//Initialisation.
String pmmlFile = "ScikitLearnNew.pmml";
PMML pmml = org.jpmml.model.PMMLUtil.unmarshal(new FileInputStream(pmmlFile));
ModelEvaluatorFactory modelEvaluatorFactory = ModelEvaluatorFactory.newInstance();
MiningModelEvaluator evaluator = (MiningModelEvaluator) modelEvaluatorFactory.newModelEvaluator(pmml);
//Determine which features are required as input
HashMap<String, Field>() inputFieldMap = new HashMap<String, Field>();
for (int i = 0; i < evaluator.getInputFields().size();i++) {
InputField curInputField = evaluator.getInputFields().get(i);
String fieldName = curInputField.getName().getValue();
inputFieldMap.put(fieldName.toLowerCase(),curInputField.getField());
}
//prediction
HashMap<String,String> argsMap = new HashMap<String,String>();
//... fill argsMap with input
Map<FieldName, ?> res;
// here we keep only features that are required by the model
Map<FieldName,String> args = new HashMap<FieldName, String>();
Iterator<String> iter = argsMap.keySet().iterator();
while (iter.hasNext()) {
String key = iter.next();
Field f = inputFieldMap.get(key);
if (f != null) {
FieldName name =f.getName();
String value = argsMap.get(key);
args.put(name, value);
}
}
//the model is applied to input, a probability distribution is obtained
res = evaluator.evaluate(args);
SegmentResult segmentResult = (SegmentResult) res;
Object targetValue = segmentResult.getTargetValue();
ProbabilityDistribution probabilityDistribution = (ProbabilityDistribution) targetValue;
You can either use a porter, I have tested the sklearn-porter (https://github.com/nok/sklearn-porter), and it works well for Java.
My code is the following:
import pandas as pd
from sklearn import tree
from sklearn_porter import Porter
train_dataset = pd.read_csv('./result2.csv').as_matrix()
X_train = train_dataset[:90, :8]
Y_train = train_dataset[:90, 8:]
X_test = train_dataset[90:, :8]
Y_test = train_dataset[90:, 8:]
print X_train.shape
print Y_train.shape
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, Y_train)
porter = Porter(clf, language='java')
output = porter.export(embed_data=True)
print(output)
In my case, I’m using a DecisionTreeClassifier, and the output of
print(output)
is the following code as text in the console:
class DecisionTreeClassifier {
private static int findMax(int[] nums) {
int index = 0;
for (int i = 0; i < nums.length; i++) {
index = nums[i] > nums[index] ? i : index;
}
return index;
}
public static int predict(double[] features) {
int[] classes = new int[2];
if (features[5] <= 51.5) {
if (features[6] <= 21.0) {
// HUGE amount of ifs..........
}
}
return findMax(classes);
}
public static void main(String[] args) {
if (args.length == 8) {
// Features:
double[] features = new double[args.length];
for (int i = 0, l = args.length; i < l; i++) {
features[i] = Double.parseDouble(args[i]);
}
// Prediction:
int prediction = DecisionTreeClassifier.predict(features);
System.out.println(prediction);
}
}
}
I found myself in a similar situation.
I’ll recommend carving out a classifier microservice. You could have a classifier microservice which runs in python and then expose calls to that service over some RESTFul API yielding JSON/XML data-interchange format. I think this is a cleaner approach.
Alternatively you can just generate a Python code from a trained model. Here is a tool that can help you with that https://github.com/BayesWitnesses/m2cgen
I have a classifier that I trained using Python’s scikit-learn. How can I use the classifier from a Java program? Can I use Jython? Is there some way to save the classifier in Python and load it in Java? Is there some other way to use it?
You cannot use jython as scikit-learn heavily relies on numpy and scipy that have many compiled C and Fortran extensions hence cannot work in jython.
The easiest ways to use scikit-learn in a java environment would be to:
-
expose the classifier as a HTTP / Json service, for instance using a microframework such as flask or bottle or cornice and call it from java using an HTTP client library
-
write a commandline wrapper application in python that reads data on stdin and output predictions on stdout using some format such as CSV or JSON (or some lower level binary representation) and call the python program from java for instance using Apache Commons Exec.
-
make the python program output the raw numerical parameters learnt at fit time (typically as an array of floating point values) and reimplement the predict function in java (this is typically easy for predictive linear models where the prediction is often just a thresholded dot product).
The last approach will be a lot more work if you need to re-implement feature extraction in Java as well.
Finally you can use a Java library such as Weka or Mahout that implement the algorithms you need instead of trying to use scikit-learn from Java.
There is JPMML project for this purpose.
First, you can serialize scikit-learn model to PMML (which is XML internally) using sklearn2pmml library directly from python or dump it in python first and convert using jpmml-sklearn in java or from a command line provided by this library. Next, you can load pmml file, deserialize and execute loaded model using jpmml-evaluator in your Java code.
This way works with not all scikit-learn models, but with many of them.
Here is some code for the JPMML solution:
–PYTHON PART–
# helper function to determine the string columns which have to be one-hot-encoded in order to apply an estimator.
def determine_categorical_columns(df):
categorical_columns = []
x = 0
for col in df.dtypes:
if col == 'object':
val = df[df.columns[x]].iloc[0]
if not isinstance(val,Decimal):
categorical_columns.append(df.columns[x])
x += 1
return categorical_columns
categorical_columns = determine_categorical_columns(df)
other_columns = list(set(df.columns).difference(categorical_columns))
#construction of transformators for our example
labelBinarizers = [(d, LabelBinarizer()) for d in categorical_columns]
nones = [(d, None) for d in other_columns]
transformators = labelBinarizers+nones
mapper = DataFrameMapper(transformators,df_out=True)
gbc = GradientBoostingClassifier()
#construction of the pipeline
lm = PMMLPipeline([
("mapper", mapper),
("estimator", gbc)
])
–JAVA PART —
//Initialisation.
String pmmlFile = "ScikitLearnNew.pmml";
PMML pmml = org.jpmml.model.PMMLUtil.unmarshal(new FileInputStream(pmmlFile));
ModelEvaluatorFactory modelEvaluatorFactory = ModelEvaluatorFactory.newInstance();
MiningModelEvaluator evaluator = (MiningModelEvaluator) modelEvaluatorFactory.newModelEvaluator(pmml);
//Determine which features are required as input
HashMap<String, Field>() inputFieldMap = new HashMap<String, Field>();
for (int i = 0; i < evaluator.getInputFields().size();i++) {
InputField curInputField = evaluator.getInputFields().get(i);
String fieldName = curInputField.getName().getValue();
inputFieldMap.put(fieldName.toLowerCase(),curInputField.getField());
}
//prediction
HashMap<String,String> argsMap = new HashMap<String,String>();
//... fill argsMap with input
Map<FieldName, ?> res;
// here we keep only features that are required by the model
Map<FieldName,String> args = new HashMap<FieldName, String>();
Iterator<String> iter = argsMap.keySet().iterator();
while (iter.hasNext()) {
String key = iter.next();
Field f = inputFieldMap.get(key);
if (f != null) {
FieldName name =f.getName();
String value = argsMap.get(key);
args.put(name, value);
}
}
//the model is applied to input, a probability distribution is obtained
res = evaluator.evaluate(args);
SegmentResult segmentResult = (SegmentResult) res;
Object targetValue = segmentResult.getTargetValue();
ProbabilityDistribution probabilityDistribution = (ProbabilityDistribution) targetValue;
You can either use a porter, I have tested the sklearn-porter (https://github.com/nok/sklearn-porter), and it works well for Java.
My code is the following:
import pandas as pd
from sklearn import tree
from sklearn_porter import Porter
train_dataset = pd.read_csv('./result2.csv').as_matrix()
X_train = train_dataset[:90, :8]
Y_train = train_dataset[:90, 8:]
X_test = train_dataset[90:, :8]
Y_test = train_dataset[90:, 8:]
print X_train.shape
print Y_train.shape
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, Y_train)
porter = Porter(clf, language='java')
output = porter.export(embed_data=True)
print(output)
In my case, I’m using a DecisionTreeClassifier, and the output of
print(output)
is the following code as text in the console:
class DecisionTreeClassifier {
private static int findMax(int[] nums) {
int index = 0;
for (int i = 0; i < nums.length; i++) {
index = nums[i] > nums[index] ? i : index;
}
return index;
}
public static int predict(double[] features) {
int[] classes = new int[2];
if (features[5] <= 51.5) {
if (features[6] <= 21.0) {
// HUGE amount of ifs..........
}
}
return findMax(classes);
}
public static void main(String[] args) {
if (args.length == 8) {
// Features:
double[] features = new double[args.length];
for (int i = 0, l = args.length; i < l; i++) {
features[i] = Double.parseDouble(args[i]);
}
// Prediction:
int prediction = DecisionTreeClassifier.predict(features);
System.out.println(prediction);
}
}
}
I found myself in a similar situation.
I’ll recommend carving out a classifier microservice. You could have a classifier microservice which runs in python and then expose calls to that service over some RESTFul API yielding JSON/XML data-interchange format. I think this is a cleaner approach.
Alternatively you can just generate a Python code from a trained model. Here is a tool that can help you with that https://github.com/BayesWitnesses/m2cgen