import multiprocessing
import multiprocessing.pool
import nose.tools as ntools
import numpy
from smqtk.algorithms.classifier.libsvm import LibSvmClassifier
from smqtk.representation import \
ClassificationElementFactory, \
DescriptorElementFactory
from smqtk.representation.classification_element.memory import \
MemoryClassificationElement
from smqtk.representation.descriptor_element.local_elements import \
DescriptorMemoryElement
if LibSvmClassifier.is_usable():
class TestLibSvmClassifier (unittest.TestCase):
def tearDown(self):
# Clear MemoryElement content
DescriptorMemoryElement.MEMORY_CACHE = {}
def test_simple_classification(self):
"""
Test libSVM classification functionality using random constructed
data, training the y=0.5 split
"""
DIM = 2
N = 1000
POS_LABEL = 'positive'
import multiprocessing
import multiprocessing.pool
import nose.tools as ntools
import numpy
from smqtk.algorithms.classifier import get_classifier_impls
from smqtk.algorithms.classifier.libsvm import LibSvmClassifier
from smqtk.representation import \
ClassificationElementFactory, \
DescriptorElementFactory
from smqtk.representation.classification_element.memory import \
MemoryClassificationElement
from smqtk.representation.descriptor_element.local_elements import \
DescriptorMemoryElement
if LibSvmClassifier.is_usable():
class TestLibSvmClassifier(unittest.TestCase):
def test_impl_findable(self):
ntools.assert_in(LibSvmClassifier.__name__, get_classifier_impls())
def test_no_save_model_pickle(self):
# Test model preservation across pickling even without model cache
# file paths set.
classifier = LibSvmClassifier(
train_params={
'-t': 0, # linear kernel
'-b': 1, # enable probability estimates
'-c': 2, # SVM-C parameter C
'-q': '', # quite mode
},
import six
from six.moves import cPickle, zip
from smqtk.algorithms.classifier import Classifier
from smqtk.algorithms.classifier.libsvm import LibSvmClassifier
from smqtk.representation import \
ClassificationElementFactory, \
DescriptorElementFactory
from smqtk.representation.classification_element.memory import \
MemoryClassificationElement
from smqtk.representation.descriptor_element.local_elements import \
DescriptorMemoryElement
from smqtk.utils.configuration import configuration_test_helper
@pytest.mark.skipif(not LibSvmClassifier.is_usable(),
reason="LibSvmClassifier does not report as usable.")
class TestLibSvmClassifier (unittest.TestCase):
def test_impl_findable(self):
self.assertIn(LibSvmClassifier, Classifier.get_impls())
@mock.patch('smqtk.algorithms.classifier.libsvm.LibSvmClassifier'
'._reload_model')
def test_configuration(self, m_inst_load_model):
""" Test configuration handling for this implementation.
Mocking out model loading when given URIs if they happen to point to
something.
"""
ex_model_uri = 'some model uri'
