converters.libsvm

Package Contents

Functions

convert(model,input_names=”input”,target_name=”target”,probability=”classProbability”,input_length=”auto”) Convert a LIBSVM model to Core ML format.
convert(model, input_names="input", target_name="target", probability="classProbability", input_length="auto")

Convert a LIBSVM model to Core ML format.

model: a libsvm model (C-SVC, nu-SVC, epsilon-SVR, or nu-SVR)
or string path to a saved model.
input_names: str | [str]
Name of the input column(s). If a single string is used (the default) the input will be an array. The length of the array will be inferred from the model, this can be overridden using the ‘input_length’ parameter.
target: str
Name of the output column.
probability: str
Name of the output class probability column. Only used for C-SVC and nu-SVC that have been trained with probability estimates enabled.
input_length: int
Set the length of the input array. This parameter should only be used when the input is an array (i.e. when ‘input_name’ is a string).
model: MLModel
Model in Core ML format.
# Make a LIBSVM model
>>> import svmutil
>>> problem = svmutil.svm_problem([0,0,1,1], [[0,1], [1,1], [8,9], [7,7]])
>>> libsvm_model = svmutil.svm_train(problem, svmutil.svm_parameter())

# Convert using default input and output names
>>> import coremltools
>>> coreml_model = coremltools.converters.libsvm.convert(libsvm_model)

# Save the CoreML model to a file.
>>> coreml_model.save('./my_model.mlmodel')

# Convert using user specified input names
>>> coreml_model = coremltools.converters.libsvm.convert(libsvm_model, input_names=['x', 'y'])