def test_get_params(self):
svm_repurposer = SvmRepurposer(self.source_model,
self.source_model_layers)
params = svm_repurposer.get_params()
expected_params = {
'context_function': 'cpu',
'num_devices': 1,
'feature_layer_names': ['fc1', 'fc2'],
'c': 1.0,
'kernel': 'linear',
'gamma': 'auto',
'enable_probability_estimates': False
}
assert params == expected_params
def test_repurpose(self, mock_model_handler):
""" Test Repurpose wrapper in meta model base class using svm repurposer object"""
mock_model_handler.return_value = RepurposerTestUtils.get_mock_model_handler_object(
)
mock_model_handler.return_value.get_layer_output.return_value = self.train_feature_dict, self.train_labels
repurposer = SvmRepurposer(self.source_model, self.source_model_layers)
self._run_common_repurposer_tests(repurposer)
def test_repurpose(self, mock_model_handler):
""" Test Repurpose wrapper in meta model base class using svm repurposer object"""
mock_model_handler.return_value = RepurposerTestUtils.get_mock_model_handler_object(
)
N = 10
train_feature_dict_subset = {
k: v[:N]
for k, v in self.train_feature_dict.items()
}
mock_model_handler.return_value.get_layer_output.return_value = train_feature_dict_subset, self.train_labels[:
N]
repurposer = SvmRepurposer(self.source_model, self.source_model_layers)
self._run_common_repurposer_tests(repurposer)
def test_train_model_from_features(self):
svm_repurposer = SvmRepurposer(self.source_model,
self.source_model_layers)
model = svm_repurposer._train_model_from_features(
self.train_features[:10], self.train_labels[:10])
self._validate_trained_model(model)