def test_pipeline_classifier_creation(self):
input_names = self.scikit_data.feature_names
p_classifier = PipelineClassifier(input_names, [1, 0])
p_classifier.add_model(self.libsvm_spec)
self.assertIsNotNone(p_classifier.spec)
self.assertEqual(
len(p_classifier.spec.pipelineClassifier.pipeline.models), 1)
# Test the model class of the svm model
spec = p_classifier.spec.pipelineClassifier.pipeline.models[0]
self.assertIsNotNone(spec.description)
# Test the interface class
self.assertEqual(spec.description.predictedFeatureName, 'target')
# Test the inputs and outputs
self.assertEqual(len(spec.description.output), 1)
self.assertEqual(spec.description.output[0].name, 'target')
self.assertEqual(spec.description.output[0].type.WhichOneof('Type'),
'int64Type')
for input_type in spec.description.input:
self.assertEqual(input_type.type.WhichOneof('Type'), 'doubleType')
self.assertEqual(sorted(input_names),
sorted(map(lambda x: x.name, spec.description.input)))
def test_pipeline_classifier_set_training_inputs(self):
builder = self.create_base_builder()
builder.spec.isUpdatable = False
training_input = [("input", datatypes.Array(3)), ("target", "String")]
# fails due to missing sub-models
p_classifier = PipelineClassifier(self.input_features,
self.output_names)
p_classifier.set_training_input(training_input)
with self.assertRaises(ValueError):
p_classifier.make_updatable()
self.assertEqual(p_classifier.spec.isUpdatable, False)
# fails due to sub-model being not updatable
p_classifier.add_model(builder.spec)
with self.assertRaises(ValueError):
p_classifier.make_updatable()
self.assertEqual(p_classifier.spec.isUpdatable, False)
builder.spec.isUpdatable = True
p_classifier.add_model(builder.spec)
self.assertEqual(p_classifier.spec.isUpdatable, False)
p_classifier.make_updatable()
self.assertEqual(p_classifier.spec.isUpdatable, True)
self.assertEqual(p_classifier.spec.description.trainingInput[0].name,
"input")
self.assertEqual(
p_classifier.spec.description.trainingInput[0].type.WhichOneof(
"Type"),
"multiArrayType",
)
self.assertEqual(p_classifier.spec.description.trainingInput[1].name,
"target")
self.assertEqual(
p_classifier.spec.description.trainingInput[1].type.WhichOneof(
"Type"),
"stringType",
)
# fails since once updatable does not allow adding new models
with self.assertRaises(ValueError):
p_classifier.add_model(builder.spec)
self.assertEqual(p_classifier.spec.isUpdatable, True)
def test_pipeline_classifier_make_updatable(self):
builder = self.create_base_builder()
builder.spec.isUpdatable = False
training_input = [('input', datatypes.Array(3)), ('target', 'String')]
# fails due to missing sub-models
p_classifier = PipelineClassifier(self.input_features,
self.output_names,
training_features=training_input)
with self.assertRaises(ValueError):
p_classifier.make_updatable()
self.assertEqual(p_classifier.spec.isUpdatable, False)
# fails due to sub-model being not updatable
p_classifier.add_model(builder.spec)
with self.assertRaises(ValueError):
p_classifier.make_updatable()
self.assertEqual(p_classifier.spec.isUpdatable, False)
builder.spec.isUpdatable = True
p_classifier.add_model(builder.spec)
self.assertEqual(p_classifier.spec.isUpdatable, False)
p_classifier.make_updatable()
self.assertEqual(p_classifier.spec.isUpdatable, True)
self.assertEqual(p_classifier.spec.description.trainingInput[0].name,
'input')
self.assertEqual(
p_classifier.spec.description.trainingInput[0].type.WhichOneof(
'Type'), 'multiArrayType')
self.assertEqual(p_classifier.spec.description.trainingInput[1].name,
'target')
self.assertEqual(
p_classifier.spec.description.trainingInput[1].type.WhichOneof(
'Type'), 'stringType')
# fails since once updatable does not allow adding new models
with self.assertRaises(ValueError):
p_classifier.add_model(builder.spec)
self.assertEqual(p_classifier.spec.isUpdatable, True)