I'm currently using new CatBoost algorithm (python version) and trying to export my model to txt file to transfer my model to C/Java implementation. Looking into documentation I have only found save_model method which is only accept two formats of file: 1. binary 2. CoreML for Apple
None of this formats is suitable for me, so maybe there is other way to achieve it?
There is no way to do this directly: Catboost doesn't support model serialization so far.
However, Catboost already can transform models to CoreML, and there is a CoreML tool to serialize models to a JSON-like text. Enjoy the minimal example:
from sklearn import datasets
iris = datasets.load_iris()
import catboost
# the shortest possible model specification
cls = catboost.CatBoostClassifier(loss_function='MultiClass', iterations=1, depth=1)
cls.fit(iris.data, iris.target)
# save model to CoreML format
cls.save_model(
"iris.mlmodel",
format="coreml",
export_parameters={
'prediction_type': 'probability'
}
)
# there is a CoreML tool for model serialization
import coremltools
model = coremltools.models.model.MLModel("iris.mlmodel")
model.get_spec()
You probably need to read coremltools documentation to fully understand what this code prints, but you can read the output like this: "There is an ensemble of a single tree with 2 leaves - in the leaf 0, class 0 dominates, in the leaf 1 - classes 1 and 2. Go to the leaf 1, if feature 3 is larger than 0.8, otherwise go to leaf 0"
specificationVersion: 1
description {
input {
name: "feature_3"
type {
doubleType {
}
}
}
output {
name: "prediction"
type {
multiArrayType {
shape: 3
dataType: DOUBLE
}
}
}
predictedFeatureName: "prediction"
predictedProbabilitiesName: "prediction"
metadata {
shortDescription: "Catboost model"
versionString: "1.0.0"
author: "Mr. Catboost Dumper"
}
}
treeEnsembleRegressor {
treeEnsemble {
nodes {
nodeBehavior: LeafNode
evaluationInfo {
evaluationValue: 0.05084745649058943
}
evaluationInfo {
evaluationIndex: 1
evaluationValue: -0.025423728245294732
}
evaluationInfo {
evaluationIndex: 2
evaluationValue: -0.025423728245294732
}
}
nodes {
nodeId: 1
nodeBehavior: LeafNode
evaluationInfo {
evaluationValue: -0.02752293516463098
}
evaluationInfo {
evaluationIndex: 1
evaluationValue: 0.01376146758231549
}
evaluationInfo {
evaluationIndex: 2
evaluationValue: 0.013761467582315471
}
}
nodes {
nodeId: 2
nodeBehavior: BranchOnValueGreaterThan
branchFeatureIndex: 3
branchFeatureValue: 0.800000011920929
trueChildNodeId: 1
}
numPredictionDimensions: 3
basePredictionValue: 0.0
basePredictionValue: 0.0
basePredictionValue: 0.0
}
postEvaluationTransform: Classification_SoftMax
}
There is one downside to this approach: CoreML doesn't support the way Catboost works with categorical features. So if you want to serialize a model with categorical features, you need to one-hot-encode them before training.
If you switch to using command line program, you can use --print-trees option. It only shows trees for the model being trained though. So you can't get trees for the existing model.
