def test_builder_with_validation(self):
builder = KNearestNeighborsClassifierBuilder(
input_name='input',
output_name='output',
number_of_dimensions=10,
default_class_label='defaultLabel',
k=3,
weighting_scheme='inverse_distance',
index_type='kd_tree',
leaf_size=50)
builder.author = 'CoreML Team'
builder.license = 'MIT'
builder.description = 'test_builder_with_validation'
# Save the updated spec
coreml_model = MLModel(builder.spec)
coreml_model_path = '/tmp/__test_builder_with_validation.mlmodel'
coreml_model.save(coreml_model_path)
self.assertTrue(os.path.isfile(coreml_model_path))
try:
stdout, stderr, return_code = self._compile_mlmodel(
coreml_model_path)
self.assertEqual(return_code, 0)
finally:
self._delete_mlmodel_and_mlmodelc(coreml_model_path)
def test_can_init_and_save_model_from_builder_with_updated_spec(self):
builder = KNearestNeighborsClassifierBuilder(
input_name="input",
output_name="output",
number_of_dimensions=10,
default_class_label="defaultLabel",
k=3,
weighting_scheme="inverse_distance",
index_type="kd_tree",
leaf_size=50,
)
builder.author = "CoreML Team"
builder.license = "MIT"
builder.description = "test_builder_with_validation"
# Save the updated spec
coreml_model = MLModel(builder.spec)
self.assertIsNotNone(coreml_model)
coreml_model_path = "/tmp/__test_builder_with_validation.mlmodel"
try:
coreml_model.save(coreml_model_path)
self.assertTrue(os.path.isfile(coreml_model_path))
finally:
self._delete_mlmodel_and_mlmodelc(coreml_model_path)
def create_builder(self, default_class_label='default_label'):
builder = KNearestNeighborsClassifierBuilder(
input_name='input',
output_name='output',
number_of_dimensions=4,
default_class_label=default_class_label)
return builder
def create_builder(self, default_class_label="default_label"):
builder = KNearestNeighborsClassifierBuilder(
input_name="input",
output_name="output",
number_of_dimensions=4,
default_class_label=default_class_label,
)
return builder
def test_can_init_and_save_model_from_builder_default_parameters(self):
builder = KNearestNeighborsClassifierBuilder(
input_name='input',
output_name='output',
number_of_dimensions=4,
default_class_label='defaultLabel')
# Save the updated spec
coreml_model = MLModel(builder.spec)
self.assertIsNotNone(coreml_model)
coreml_model_path = '/tmp/__test_builder_with_validation.mlmodel'
try:
coreml_model.save(coreml_model_path)
self.assertTrue(os.path.isfile(coreml_model_path))
finally:
self._delete_mlmodel_and_mlmodelc(coreml_model_path)
def test_builder_with_compilation_default_parameters(self):
builder = KNearestNeighborsClassifierBuilder(input_name='input',
output_name='output',
number_of_dimensions=4,
default_class_label='defaultLabel')
# Save the updated spec
coreml_model = MLModel(builder.spec)
coreml_model_path = '/tmp/__test_builder_with_validation.mlmodel'
coreml_model.save(coreml_model_path)
self.assertTrue(os.path.isfile(coreml_model_path))
try:
stdout, stderr, return_code = self._compile_mlmodel(coreml_model_path)
self.assertEqual(return_code, 0)
finally:
self._delete_mlmodel_and_mlmodelc(coreml_model_path)
del base_spec.description.output[:]
# Add a new output for the feature vector.
output = base_spec.description.output.add()
output.name = "features"
output.type.multiArrayType.shape.append(1000)
output.type.multiArrayType.dataType = ft.ArrayFeatureType.FLOAT32
# Connect the last layer to this new output.
base_spec.neuralNetwork.layers[-1].output[0] = "features"
# Create the k-NN model.
knn_builder = KNearestNeighborsClassifierBuilder(
input_name="features",
output_name="label",
number_of_dimensions=1000,
default_class_label="???",
number_of_neighbors=3,
weighting_scheme="inverse_distance",
index_type="linear")
knn_spec = knn_builder.spec
knn_spec.description.input[0].shortDescription = "Input vector"
knn_spec.description.output[0].shortDescription = "Predicted label"
knn_spec.description.output[
1].shortDescription = "Probabilities for each possible label"
knn_builder.set_number_of_neighbors_with_bounds(3, allowed_range=(1, 10))
# Use the same name as in the neural network models, so that we
# can use the same code for evaluating both types of model.
knn_spec.description.predictedProbabilitiesName = "labelProbability"
