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'
ex_labelmap_uri = 'some label map uri'
ex_trainparams = {'-s': 8, '-t': -10, '-b': 42, '-c': 7.2}
ex_normalize = 2
c = LibSvmClassifier(ex_model_uri, ex_labelmap_uri,
train_params=ex_trainparams,
normalize=ex_normalize)
for inst in configuration_test_helper(c): # type: LibSvmClassifier
assert inst.svm_model_uri == ex_model_uri
assert inst.svm_label_map_uri == ex_labelmap_uri
assert inst.train_params == ex_trainparams
assert inst.normalize == ex_normalize
def test_simple_classification(self):
"""
simple LibSvmClassifier test - 2-class
Test libSVM classification functionality using random constructed
data, training the y=0.5 split
"""
DIM = 2
N = 1000
POS_LABEL = 'positive'
NEG_LABEL = 'negative'
p = multiprocessing.pool.ThreadPool()
d_factory = DescriptorElementFactory(DescriptorMemoryElement, {})
c_factory = ClassificationElementFactory(
MemoryClassificationElement, {})
def make_element(argtup):
(i, v) = argtup
d = d_factory.new_descriptor('test', i)
d.set_vector(v)
return d
# Constructing artificial descriptors
x = numpy.random.rand(N, DIM)
x_pos = x[x[:, 1] <= 0.45]
x_neg = x[x[:, 1] >= 0.55]
d_pos = p.map(make_element, enumerate(x_pos))
d_neg = p.map(make_element, enumerate(x_neg, start=N // 2))
# Create/Train test classifier
classifier = LibSvmClassifier(
train_params={
'-t': 0, # linear kernel
'-b': 1, # enable probability estimates
'-c': 2, # SVM-C parameter C
'-q': '', # quite mode
},
normalize=None, # DO NOT normalize descriptors
)
classifier.train({POS_LABEL: d_pos, NEG_LABEL: d_neg})
# Test classifier
x = numpy.random.rand(N, DIM)
x_pos = x[x[:, 1] <= 0.45]
x_neg = x[x[:, 1] >= 0.55]
d_pos = p.map(make_element, enumerate(x_pos, N))
d_neg = p.map(make_element, enumerate(x_neg, N + N // 2))
d_pos_sync = {} # for comparing to async
for d in d_pos:
c = classifier.classify(d, c_factory)
ntools.assert_equal(
c.max_label(), POS_LABEL,
"Found False positive: %s :: %s" %
(d.vector(), c.get_classification()))
d_pos_sync[d] = c
d_neg_sync = {}
for d in d_neg:
c = classifier.classify(d, c_factory)
ntools.assert_equal(
c.max_label(), NEG_LABEL,
"Found False negative: %s :: %s" %
(d.vector(), c.get_classification()))
d_neg_sync[d] = c
# test that async classify produces the same results
# -- d_pos
m_pos = classifier.classify_async(d_pos, c_factory)
ntools.assert_equal(
m_pos, d_pos_sync,
"Async computation of pos set did not yield "
"the same results as synchronous "
"classification.")
# -- d_neg
m_neg = classifier.classify_async(d_neg, c_factory)
ntools.assert_equal(
m_neg, d_neg_sync,
"Async computation of neg set did not yield "
"the same results as synchronous "
"classification.")
# -- combined -- threaded
combined_truth = dict(d_pos_sync.items())
combined_truth.update(d_neg_sync)
m_combined = classifier.classify_async(
d_pos + d_neg,
c_factory,
use_multiprocessing=False,
)
ntools.assert_equal(
m_combined, combined_truth,
"Async computation of all test descriptors "
"did not yield the same results as "
"synchronous classification.")
# -- combined -- multiprocess
m_combined = classifier.classify_async(
d_pos + d_neg,
c_factory,
use_multiprocessing=True,
)
ntools.assert_equal(
m_combined, combined_truth,
"Async computation of all test descriptors "
"(mixed order) did not yield the same results "
"as synchronous classification.")
# Closing resources
p.close()
p.join()
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
},
normalize=None, # DO NOT normalize descriptors
)
ntools.assert_true(classifier.svm_model is None)
# Empty model should not trigger __LOCAL__ content in pickle
ntools.assert_not_in('__LOCAL__', classifier.__getstate__())
_ = cPickle.loads(cPickle.dumps(classifier))
# train arbitrary model (same as ``test_simple_classification``)
DIM = 2
N = 1000
POS_LABEL = 'positive'
NEG_LABEL = 'negative'
d_factory = DescriptorElementFactory(DescriptorMemoryElement, {})
c_factory = ClassificationElementFactory(
MemoryClassificationElement, {})
def make_element(argtup):
(i, v) = argtup
d = d_factory.new_descriptor('test', i)
d.set_vector(v)
return d
# Constructing artificial descriptors
x = numpy.random.rand(N, DIM)
x_pos = x[x[:, 1] <= 0.45]
x_neg = x[x[:, 1] >= 0.55]
p = multiprocessing.pool.ThreadPool()
d_pos = p.map(make_element, enumerate(x_pos))
d_neg = p.map(make_element, enumerate(x_neg, start=N // 2))
p.close()
p.join()
# Training
classifier.train({POS_LABEL: d_pos, NEG_LABEL: d_neg})
# Test original classifier
t_v = numpy.random.rand(DIM)
t = d_factory.new_descriptor('query', 0)
t.set_vector(t_v)
c_expected = classifier.classify(t, c_factory)
# Should see __LOCAL__ content in pickle state now
p_state = classifier.__getstate__()
ntools.assert_in('__LOCAL__', p_state)
ntools.assert_in('__LOCAL_LABELS__', p_state)
ntools.assert_in('__LOCAL_MODEL__', p_state)
ntools.assert_true(len(p_state['__LOCAL_LABELS__']) > 0)
ntools.assert_true(len(p_state['__LOCAL_MODEL__']) > 0)
# Restored classifier should classify the same test descriptor the
# same
#: :type: LibSvmClassifier
classifier2 = cPickle.loads(cPickle.dumps(classifier))
c_post_pickle = classifier2.classify(t, c_factory)
# There may be floating point error, so extract actual confidence
# values and check post round
c_pp_positive = c_post_pickle[POS_LABEL]
c_pp_negative = c_post_pickle[NEG_LABEL]
c_e_positive = c_expected[POS_LABEL]
c_e_negative = c_expected[NEG_LABEL]
ntools.assert_almost_equal(c_e_positive, c_pp_positive, 5)
ntools.assert_almost_equal(c_e_negative, c_pp_negative, 5)
def test_simple_multiclass_classification(self):
"""
simple LibSvmClassifier test - 3-class
Test libSVM classification functionality using random constructed
data, training the y=0.33 and y=.66 split
"""
DIM = 2
N = 1000
P1_LABEL = 'p1'
P2_LABEL = 'p2'
P3_LABEL = 'p3'
p = multiprocessing.pool.ThreadPool()
d_factory = DescriptorElementFactory(DescriptorMemoryElement, {})
c_factory = ClassificationElementFactory(
MemoryClassificationElement, {})
di = 0
def make_element(argtup):
(i, v) = argtup
d = d_factory.new_descriptor('test', i)
d.set_vector(v)
return d
# Constructing artificial descriptors
x = numpy.random.rand(N, DIM)
x_p1 = x[x[:, 1] <= 0.30]
x_p2 = x[(x[:, 1] >= 0.36) & (x[:, 1] <= 0.63)]
x_p3 = x[x[:, 1] >= 0.69]
d_p1 = p.map(make_element, enumerate(x_p1, di))
di += len(d_p1)
d_p2 = p.map(make_element, enumerate(x_p2, di))
di += len(d_p2)
d_p3 = p.map(make_element, enumerate(x_p3, di))
di += len(d_p3)
# Create/Train test classifier
classifier = LibSvmClassifier(
train_params={
'-t': 0, # linear kernel
'-b': 1, # enable probability estimates
'-c': 2, # SVM-C parameter C
'-q': '' # quite mode
},
normalize=None, # DO NOT normalize descriptors
)
classifier.train({P1_LABEL: d_p1, P2_LABEL: d_p2, P3_LABEL: d_p3})
# Test classifier
x = numpy.random.rand(N, DIM)
x_p1 = x[x[:, 1] <= 0.30]
x_p2 = x[(x[:, 1] >= 0.36) & (x[:, 1] <= 0.63)]
x_p3 = x[x[:, 1] >= 0.69]
d_p1 = p.map(make_element, enumerate(x_p1, di))
di += len(d_p1)
d_p2 = p.map(make_element, enumerate(x_p2, di))
di += len(d_p2)
d_p3 = p.map(make_element, enumerate(x_p3, di))
di += len(d_p3)
d_p1_sync = {}
for d in d_p1:
c = classifier.classify(d, c_factory)
ntools.assert_equal(
c.max_label(), P1_LABEL, "Incorrect %s label: %s :: %s" %
(P1_LABEL, d.vector(), c.get_classification()))
d_p1_sync[d] = c
d_p2_sync = {}
for d in d_p2:
c = classifier.classify(d, c_factory)
ntools.assert_equal(
c.max_label(), P2_LABEL, "Incorrect %s label: %s :: %s" %
(P2_LABEL, d.vector(), c.get_classification()))
d_p2_sync[d] = c
d_neg_sync = {}
for d in d_p3:
c = classifier.classify(d, c_factory)
ntools.assert_equal(
c.max_label(), P3_LABEL, "Incorrect %s label: %s :: %s" %
(P3_LABEL, d.vector(), c.get_classification()))
d_neg_sync[d] = c
# test that async classify produces the same results
# -- p1
async_p1 = classifier.classify_async(d_p1, c_factory)
ntools.assert_equal(
async_p1, d_p1_sync,
"Async computation of p1 set did not yield "
"the same results as synchronous computation.")
# -- p2
async_p2 = classifier.classify_async(d_p2, c_factory)
ntools.assert_equal(
async_p2, d_p2_sync,
"Async computation of p2 set did not yield "
"the same results as synchronous computation.")
# -- neg
async_neg = classifier.classify_async(d_p3, c_factory)
ntools.assert_equal(
async_neg, d_neg_sync,
"Async computation of neg set did not yield "
"the same results as synchronous computation.")
# -- combined -- threaded
sync_combined = dict(d_p1_sync.items())
sync_combined.update(d_p2_sync)
sync_combined.update(d_neg_sync)
async_combined = classifier.classify_async(
d_p1 + d_p2 + d_p3, c_factory, use_multiprocessing=False)
ntools.assert_equal(
async_combined, sync_combined,
"Async computation of all test descriptors "
"did not yield the same results as "
"synchronous classification.")
# -- combined -- multiprocess
async_combined = classifier.classify_async(
d_p1 + d_p2 + d_p3, c_factory, use_multiprocessing=True)
ntools.assert_equal(
async_combined, sync_combined,
"Async computation of all test descriptors "
"(mixed order) did not yield the same results "
"as synchronous classification.")
# Closing resources
p.close()
p.join()
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
},
def test_simple_multiclass_classification(self):
"""
Test libSVM classification functionality using random constructed
data, training the y=0.33 and y=.66 split
"""
DIM = 2
N = 1000
P1_LABEL = 'p1'
P2_LABEL = 'p2'
p = multiprocessing.pool.ThreadPool()
d_factory = DescriptorElementFactory(DescriptorMemoryElement, {})
c_factory = ClassificationElementFactory(MemoryClassificationElement, {})
di = 0
def make_element((i, v)):
d = d_factory.new_descriptor('test', i)
d.set_vector(v)
return d
# Constructing artificial descriptors
x = numpy.random.rand(N, DIM)
x_p1 = x[x[:, 1] <= 0.30]
x_p2 = x[(x[:, 1] >= 0.36) & (x[:, 1] <= 0.63)]
x_neg = x[x[:, 1] >= 0.69]
d_p1 = p.map(make_element, enumerate(x_p1, di))
di += len(d_p1)
d_p2 = p.map(make_element, enumerate(x_p2, di))
di += len(d_p2)
d_neg = p.map(make_element, enumerate(x_neg, di))
di += len(d_neg)
# Create/Train test classifier
classifier = LibSvmClassifier(
train_params={
'-t': 0, # linear kernel
'-b': 1, # enable probability estimates
'-c': 2, # SVM-C parameter C
'-q': '' # quite mode
},
normalize=None, # DO NOT normalize descriptors
)
classifier.train({P1_LABEL: d_p1, P2_LABEL: d_p2}, d_neg)
# Test classifier
x = numpy.random.rand(N, DIM)
x_p1 = x[x[:, 1] <= 0.30]
x_p2 = x[(x[:, 1] >= 0.36) & (x[:, 1] <= 0.63)]
x_neg = x[x[:, 1] >= 0.69]
d_p1 = p.map(make_element, enumerate(x_p1, di))
di += len(d_p1)
d_p2 = p.map(make_element, enumerate(x_p2, di))
di += len(d_p2)
d_neg = p.map(make_element, enumerate(x_neg, di))
di += len(d_neg)
for d in d_p1:
c = classifier.classify(d, c_factory)
ntools.assert_equal(c.max_label(),
P1_LABEL,
"Incorrect %s label: %s :: %s" %
(P1_LABEL, d.vector(),
c.get_classification()))
for d in d_p2:
c = classifier.classify(d, c_factory)
ntools.assert_equal(c.max_label(),
P2_LABEL,
"Incorrect %s label: %s :: %s" %
(P2_LABEL, d.vector(),
c.get_classification()))
for d in d_neg:
c = classifier.classify(d, c_factory)
ntools.assert_equal(c.max_label(),
LibSvmClassifier.NEGATIVE_LABEL,
"Incorrect %s label: %s :: %s" %
(LibSvmClassifier.NEGATIVE_LABEL,
d.vector(),
c.get_classification()))
# Closing resources
p.close()
p.join()
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'
p = multiprocessing.pool.ThreadPool()
d_factory = DescriptorElementFactory(DescriptorMemoryElement, {})
c_factory = ClassificationElementFactory(MemoryClassificationElement, {})
def make_element((i, v)):
d = d_factory.new_descriptor('test', i)
d.set_vector(v)
return d
# Constructing artificial descriptors
x = numpy.random.rand(N, DIM)
x_pos = x[x[:, 1] <= 0.45]
x_neg = x[x[:, 1] >= 0.55]
d_pos = p.map(make_element, enumerate(x_pos))
d_neg = p.map(make_element, enumerate(x_neg, start=N//2))
# Create/Train test classifier
classifier = LibSvmClassifier(
train_params={
'-t': 0, # linear kernel
'-b': 1, # enable probability estimates
'-c': 2, # SVM-C parameter C
'-q': '', # quite mode
},
normalize=None, # DO NOT normalize descriptors
)
classifier.train({POS_LABEL: d_pos}, d_neg)
# Test classifier
x = numpy.random.rand(N, DIM)
x_pos = x[x[:, 1] <= 0.45]
x_neg = x[x[:, 1] >= 0.55]
d_pos = p.map(make_element, enumerate(x_pos, N))
d_neg = p.map(make_element, enumerate(x_neg, N + N//2))
for d in d_pos:
c = classifier.classify(d, c_factory)
ntools.assert_equal(c.max_label(),
POS_LABEL,
"Found False positive: %s :: %s" %
(d.vector(), c.get_classification()))
for d in d_neg:
c = classifier.classify(d, c_factory)
ntools.assert_equal(c.max_label(),
LibSvmClassifier.NEGATIVE_LABEL,
"Found False negative: %s :: %s" %
(d.vector(), c.get_classification()))
# Closing resources
p.close()
p.join()
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'
def test_simple_multiclass_classification(self):
"""
simple LibSvmClassifier test - 3-class
Test libSVM classification functionality using random constructed
data, training the y=0.33 and y=.66 split
"""
DIM = 2
N = 1000
P1_LABEL = 'p1'
P2_LABEL = 'p2'
P3_LABEL = 'p3'
p = multiprocessing.pool.ThreadPool()
d_factory = DescriptorElementFactory(DescriptorMemoryElement, {})
di = 0
def make_element(iv):
i, v = iv
elem = d_factory.new_descriptor('test', i)
elem.set_vector(v)
return elem
# Constructing artificial descriptors
x = numpy.random.rand(N, DIM)
x_p1 = x[x[:, 1] <= 0.30]
x_p2 = x[(x[:, 1] >= 0.36) & (x[:, 1] <= 0.63)]
x_p3 = x[x[:, 1] >= 0.69]
d_p1 = p.map(make_element, enumerate(x_p1, di))
di += len(d_p1)
d_p2 = p.map(make_element, enumerate(x_p2, di))
di += len(d_p2)
d_p3 = p.map(make_element, enumerate(x_p3, di))
di += len(d_p3)
# Create/Train test classifier
classifier = LibSvmClassifier(
train_params={
'-t': 0, # linear kernel
'-b': 1, # enable probability estimates
'-c': 2, # SVM-C parameter C
'-q': '' # quite mode
},
normalize=None, # DO NOT normalize descriptors
)
classifier.train({P1_LABEL: d_p1, P2_LABEL: d_p2, P3_LABEL: d_p3})
# Test classifier
x = numpy.random.rand(N, DIM)
x_p1 = x[x[:, 1] <= 0.30]
x_p2 = x[(x[:, 1] >= 0.36) & (x[:, 1] <= 0.63)]
x_p3 = x[x[:, 1] >= 0.69]
# Test that examples expected to classify to certain classes are.
c_map_p1 = list(classifier._classify_arrays(x_p1))
for v, c_map in zip(x_p1, c_map_p1):
assert c_map[P1_LABEL] > max(c_map[P2_LABEL], c_map[P3_LABEL]), \
"Incorrect {} label: {} :: {}".format(P1_LABEL, v, c_map)
c_map_p2 = list(classifier._classify_arrays(x_p2))
for v, c_map in zip(x_p2, c_map_p2):
assert c_map[P2_LABEL] > max(c_map[P1_LABEL], c_map[P3_LABEL]), \
"Incorrect {} label: {} :: {}".format(P2_LABEL, v, c_map)
c_map_p3 = list(classifier._classify_arrays(x_p3))
for v, c_map in zip(x_p3, c_map_p3):
assert c_map[P3_LABEL] > max(c_map[P1_LABEL], c_map[P2_LABEL]), \
"Incorrect {} label: {} :: {}".format(P3_LABEL, v, c_map)
# Closing resources
p.close()
p.join()
def test_simple_classification(self):
"""
simple LibSvmClassifier test - 2-class
Test libSVM classification functionality using random constructed
data, training the y=0.5 split
"""
DIM = 2
N = 1000
POS_LABEL = 'positive'
NEG_LABEL = 'negative'
p = multiprocessing.pool.ThreadPool()
d_factory = DescriptorElementFactory(DescriptorMemoryElement, {})
def make_element(iv):
i, v = iv
elem = d_factory.new_descriptor('test', i)
elem.set_vector(v)
return elem
# Constructing artificial descriptors
x = numpy.random.rand(N, DIM)
x_pos = x[x[:, 1] <= 0.45]
x_neg = x[x[:, 1] >= 0.55]
d_pos = p.map(make_element, enumerate(x_pos))
d_neg = p.map(make_element, enumerate(x_neg, start=N//2))
# Create/Train test classifier
classifier = LibSvmClassifier(
train_params={
'-t': 0, # linear kernel
'-b': 1, # enable probability estimates
'-c': 2, # SVM-C parameter C
'-q': '', # quite mode
},
normalize=None, # DO NOT normalize descriptors
)
classifier.train({POS_LABEL: d_pos, NEG_LABEL: d_neg})
# Test classifier
x = numpy.random.rand(N, DIM)
x_pos = x[x[:, 1] <= 0.45]
x_neg = x[x[:, 1] >= 0.55]
# Test that examples expected to classify to the positive class are,
# and same for those expected to be in the negative class.
c_map_pos = list(classifier._classify_arrays(x_pos))
for v, c_map in zip(x_pos, c_map_pos):
assert c_map[POS_LABEL] > c_map[NEG_LABEL], \
"Found False positive: {} :: {}" \
.format(v, c_map)
c_map_neg = list(classifier._classify_arrays(x_neg))
for v, c_map in zip(x_neg, c_map_neg):
assert c_map[NEG_LABEL] > c_map[POS_LABEL], \
"Found False negative: {} :: {}" \
.format(v, c_map)
# Closing resources
p.close()
p.join()
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'