def test_transform_changed_domain(self):
"""
1. Open data, apply some preprocessor, splits the data into two parts,
use LDA on the first part, and then transform the second part.
2. Open data, split into two parts, apply the same preprocessor and
LDA only on the first part, and then transform the second part.
The transformed second part in (1) and (2) has to be the same.
"""
data = Table("iris")
data = Randomize()(data)
preprocessor = Continuize()
lda = LDA()
# normalize all
ndata = preprocessor(data)
model = lda(ndata[:75])
result_1 = model(ndata[75:])
# normalize only the "training" part
ndata = preprocessor(data[:75])
model = lda(ndata)
result_2 = model(data[75:])
np.testing.assert_almost_equal(result_1.X, result_2.X)
def test_lda(self):
iris = Table('iris')
n_components = 2
lda = LDA(solver="eigen", n_components=n_components)
model = lda(iris)
transformed = model(iris)
self.assertEqual(transformed.X.shape, (len(iris), n_components))
self.assertEqual(transformed.Y.shape, (len(iris),))
def test_lda(self):
iris = Table("iris")
n_components = 2
lda = LDA(n_components=n_components)
model = lda(iris)
transformed = model(iris)
self.assertEqual(transformed.X.shape, (len(iris), n_components))
self.assertEqual(transformed.Y.shape, (len(iris),))
def init_projection(self):
if self.placement == Placement.Circular:
self.projector = CircularPlacement()
elif self.placement == Placement.LDA:
self.projector = LDA(solver="eigen", n_components=2)
elif self.placement == Placement.PCA:
self.projector = PCA(n_components=2)
self.projector.component = 2
self.projector.preprocessors = PCA.preprocessors + [Normalize()]
super().init_projection()