class TestClassifierFunctionality(unittest.TestCase):
def setUp(self):
self.nn = MLPC(layers=[L("Linear")], n_iter=1)
def test_FitAutoInitialize(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_ExplicitValidSet(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.valid_set = (a_in, a_out)
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_PartialFit(self):
a_in, a_out = numpy.zeros((8,4)), numpy.random.randint(0, 5, (8,))
self.nn.partial_fit(a_in, a_out, classes=[0,1,2,3])
self.nn.partial_fit(a_in*2.0, a_out+1, classes=[0,1,2,3])
def test_PredictUninitializedNoUnitCount(self):
a_in = numpy.zeros((8,16))
assert_raises(AssertionError, self.nn.predict, a_in)
def test_PredictUninitializedNoLabels(self):
self.nn.layers[-1].units = 4
a_in = numpy.zeros((8,16))
assert_raises(AssertionError, self.nn.predict, a_in)
def test_PredictClasses(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape[0], a_test.shape[0])
def test_PredictMultiClass(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 3, (8,2))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape, a_test.shape)
def test_EstimateProbalities(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict_proba(a_in)
assert_equal(type(a_out), type(a_test))
def test_CalculateScore(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.fit(a_in, a_out)
f = self.nn.score(a_in, a_out)
assert_equal(type(f), numpy.float64)
class TestClassifierFunctionality(unittest.TestCase):
def setUp(self):
self.nn = MLPC(layers=[L("Linear")], n_iter=1)
def test_FitAutoInitialize(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_ExplicitValidSet(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.valid_set = (a_in, a_out)
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_PartialFit(self):
a_in, a_out = numpy.zeros((8, 4)), numpy.random.randint(0, 5, (8, ))
self.nn.partial_fit(a_in, a_out, classes=[0, 1, 2, 3])
self.nn.partial_fit(a_in * 2.0, a_out + 1, classes=[0, 1, 2, 3])
def test_PredictUninitializedNoUnitCount(self):
a_in = numpy.zeros((8, 16))
assert_raises(AssertionError, self.nn.predict, a_in)
def test_PredictUninitializedNoLabels(self):
self.nn.layers[-1].units = 4
a_in = numpy.zeros((8, 16))
assert_raises(AssertionError, self.nn.predict, a_in)
def test_PredictClasses(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape[0], a_test.shape[0])
def test_PredictMultiClass(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 3, (8, 2))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape, a_test.shape)
def test_EstimateProbalities(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict_proba(a_in)
assert_equal(type(a_out), type(a_test))
def test_CalculateScore(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.fit(a_in, a_out)
f = self.nn.score(a_in, a_out)
assert_equal(type(f), numpy.float64)
class TestClassifierFunctionality(unittest.TestCase):
def setUp(self):
self.nn = MLPC(layers=[L("Linear")], n_iter=1)
def test_FitAutoInitialize(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, ),
dtype=numpy.int32)
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_PartialFit(self):
a_in, a_out = numpy.zeros((8, 4)), numpy.zeros((8, ),
dtype=numpy.int32)
self.nn.partial_fit(a_in, a_out, classes=[0, 1, 2, 3])
self.nn.partial_fit(a_in * 2.0, a_out + 1, classes=[0, 1, 2, 3])
def test_PredictUninitialized(self):
a_in = numpy.zeros((8, 16))
assert_raises(ValueError, self.nn.predict, a_in)
def test_PredictClasses(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, ),
dtype=numpy.int32)
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape, a_test.shape)
def test_EstimateProbalities(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, ),
dtype=numpy.int32)
self.nn.fit(a_in, a_out)
a_test = self.nn.predict_proba(a_in)
assert_equal(type(a_out), type(a_test))
def test_CalculateScore(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, ),
dtype=numpy.int32)
self.nn.fit(a_in, a_out)
f = self.nn.score(a_in, a_out)
assert_equal(type(f), numpy.float64)
class TestClassifierFunctionality(unittest.TestCase):
def setUp(self):
self.nn = MLPC(layers=[L("Linear")], n_iter=1)
def test_FitAutoInitialize(self):
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,), dtype=numpy.int32)
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_PartialFit(self):
a_in, a_out = numpy.zeros((8,4)), numpy.zeros((8,), dtype=numpy.int32)
self.nn.partial_fit(a_in, a_out, classes=[0,1,2,3])
self.nn.partial_fit(a_in*2.0, a_out+1, classes=[0,1,2,3])
def test_PredictUninitialized(self):
a_in = numpy.zeros((8,16))
assert_raises(ValueError, self.nn.predict, a_in)
def test_PredictClasses(self):
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,), dtype=numpy.int32)
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape, a_test.shape)
def test_EstimateProbalities(self):
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,), dtype=numpy.int32)
self.nn.fit(a_in, a_out)
a_test = self.nn.predict_proba(a_in)
assert_equal(type(a_out), type(a_test))
def test_CalculateScore(self):
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,), dtype=numpy.int32)
self.nn.fit(a_in, a_out)
f = self.nn.score(a_in, a_out)
assert_equal(type(f), numpy.float64)
class TestClassifierFunctionality(unittest.TestCase):
def setUp(self):
self.nn = MLPC(layers=[L("Softmax")], n_iter=1)
def test_IsClassifier(self):
assert_true(self.nn.is_classifier)
def test_FitAutoInitialize(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_ExplicitValidSet(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.valid_set = (a_in, a_out)
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_PartialFit(self):
a_in, a_out = numpy.zeros((8, 4)), numpy.random.randint(0, 5, (8, ))
self.nn.partial_fit(a_in, a_out, classes=[0, 1, 2, 3])
self.nn.partial_fit(a_in * 2.0, a_out + 1, classes=[0, 1, 2, 3])
def test_PredictUninitializedNoUnitCount(self):
a_in = numpy.zeros((8, 16))
assert_raises(AssertionError, self.nn.predict, a_in)
def test_PredictUninitializedNoLabels(self):
self.nn.layers[-1].units = 4
a_in = numpy.zeros((8, 16))
assert_raises(AssertionError, self.nn.predict, a_in)
def test_PredictBinaryProbability(self):
a_in = numpy.random.uniform(-1.0, 1.0, size=(8, 16))
a_out = numpy.array((a_in.sum(axis=1) >= 0.0), dtype=numpy.int32)
a_out[0], a_out[-1] = 0, 1
self.nn.fit(a_in, a_out)
a_proba = self.nn.predict_proba(a_in)
a_test = self.nn.predict(a_in)
c_out = numpy.unique(a_out)
assert_equal(2, c_out.shape[0])
assert_equal((8, 2), a_proba.shape)
def test_PredictClasses(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.fit(a_in, a_out)
self.nn.batch_size = 4
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape[0], a_test.shape[0])
c_out = numpy.unique(a_out)
assert_equal(len(self.nn.classes_), 1)
assert_true((self.nn.classes_[0] == c_out).all())
def test_PredictLargerBatchSize(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, 1))
self.nn.batch_size = 32
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape[0], a_test.shape[0])
def test_PredictMultiClass(self):
a_in, a_out = numpy.zeros(
(32, 16)), numpy.random.randint(0, 3, (32, 2))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape, a_test.shape)
assert_equal(len(self.nn.classes_), 2)
assert_equal(self.nn.classes_[0].shape[0], 3)
assert_equal(self.nn.classes_[1].shape[0], 3)
def test_EstimateProbalities(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict_proba(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_in.shape[0], a_test.shape[0])
def test_CalculateScore(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.random.randint(0, 5, (8, ))
self.nn.fit(a_in, a_out)
f = self.nn.score(a_in, a_out)
assert_equal(type(f), numpy.float64)
class TestClassifierFunctionality(unittest.TestCase):
def setUp(self):
self.nn = MLPC(layers=[L("Softmax")], n_iter=1)
def test_IsClassifier(self):
assert_true(self.nn.is_classifier)
def test_FitAutoInitialize(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_ExplicitValidSet(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.valid_set = (a_in, a_out)
self.nn.fit(a_in, a_out)
assert_true(self.nn.is_initialized)
def test_PartialFit(self):
a_in, a_out = numpy.zeros((8,4)), numpy.random.randint(0, 5, (8,))
self.nn.partial_fit(a_in, a_out, classes=[0,1,2,3])
self.nn.partial_fit(a_in*2.0, a_out+1, classes=[0,1,2,3])
def test_PredictUninitializedNoUnitCount(self):
a_in = numpy.zeros((8,16))
assert_raises(AssertionError, self.nn.predict, a_in)
def test_PredictUninitializedNoLabels(self):
self.nn.layers[-1].units = 4
a_in = numpy.zeros((8,16))
assert_raises(AssertionError, self.nn.predict, a_in)
def test_PredictBinaryProbability(self):
a_in = numpy.random.uniform(-1.0, 1.0, size=(8,16))
a_out = numpy.array((a_in.sum(axis=1) >= 0.0), dtype=numpy.int32)
a_out[0], a_out[-1] = 0, 1
self.nn.fit(a_in, a_out)
a_proba = self.nn.predict_proba(a_in)
a_test = self.nn.predict(a_in)
c_out = numpy.unique(a_out)
assert_equal(2, c_out.shape[0])
assert_equal((8, 2), a_proba.shape)
assert_true((a_proba >= 0.0).all())
assert_true((a_proba <= 1.0).all())
assert_true((abs(a_proba.sum(axis=1) - 1.0) < 1E-9).all())
def test_PredictClasses(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.fit(a_in, a_out)
self.nn.batch_size = 4
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape[0], a_test.shape[0])
c_out = numpy.unique(a_out)
assert_equal(len(self.nn.classes_), 1)
assert_true((self.nn.classes_[0] == c_out).all())
def test_PredictLargerBatchSize(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,1))
self.nn.batch_size = 32
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape[0], a_test.shape[0])
def test_PredictMultiClass(self):
a_in, a_out = numpy.zeros((32,16)), numpy.random.randint(0, 3, (32,2))
self.nn.fit(a_in, a_out)
a_test = self.nn.predict(a_in)
assert_equal(type(a_out), type(a_test))
assert_equal(a_out.shape, a_test.shape)
assert_equal(len(self.nn.classes_), 2)
assert_equal(self.nn.classes_[0].shape[0], 3)
assert_equal(self.nn.classes_[1].shape[0], 3)
def test_EstimateProbalities(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.fit(a_in, a_out)
a_proba = self.nn.predict_proba(a_in)
assert_equal(type(a_out), type(a_proba))
assert_equal(a_in.shape[0], a_proba.shape[0])
assert_true((a_proba >= 0.0).all())
assert_true((a_proba <= 1.0).all())
assert_true((abs(a_proba.sum(axis=1) - 1.0) < 1E-9).all())
def test_MultipleProbalitiesAsList(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,4))
self.nn.fit(a_in, a_out)
a_proba = self.nn.predict_proba(a_in)
assert_equal(list, type(a_proba))
assert_equal(4, len(a_proba))
for p in a_proba:
assert_equal(a_in.shape[0], p.shape[0])
assert_less_equal(p.shape[1], 5)
assert_true((p >= 0.0).all())
assert_true((p <= 1.0).all())
assert_true((abs(p.sum(axis=1) - 1.0) < 1E-9).all())
def test_CalculateScore(self):
a_in, a_out = numpy.zeros((8,16)), numpy.random.randint(0, 5, (8,))
self.nn.fit(a_in, a_out)
f = self.nn.score(a_in, a_out)
assert_equal(type(f), numpy.float64)
