def test_n_componets_from_reducer():
from pymks import MKSStructureAnalysis
from pymks import DiscreteIndicatorBasis
from sklearn.manifold import LocallyLinearEmbedding
reducer = LocallyLinearEmbedding(n_components=7)
dbasis = DiscreteIndicatorBasis(n_states=3, domain=[0, 2])
model = MKSStructureAnalysis(dimension_reducer=reducer, basis=dbasis)
assert model.n_components == 7
def test_set_correlations():
from pymks import PrimitiveBasis
from pymks import MKSStructureAnalysis
test_correlations = [(0, 0), (0, 2), (0, 4)]
prim_basis = PrimitiveBasis(6)
model_prim = MKSStructureAnalysis(basis=prim_basis,
correlations=test_correlations)
assert model_prim.correlations == test_correlations
def test_n_components_with_reducer():
from pymks import MKSStructureAnalysis
from pymks import DiscreteIndicatorBasis
from sklearn.manifold import Isomap
reducer = Isomap(n_components=7)
dbasis = DiscreteIndicatorBasis(n_states=3, domain=[0, 2])
model = MKSStructureAnalysis(dimension_reducer=reducer, basis=dbasis,
n_components=9)
assert model.n_components == 9
def test_set_components():
from pymks import MKSStructureAnalysis
from pymks import PrimitiveBasis
p_basis = PrimitiveBasis(2)
model = MKSStructureAnalysis(basis=p_basis)
X = np.random.randint(2, size=(50, 10, 10))
model.fit(X)
components = model.components_
model.components_ = components * 2
assert np.allclose(model.components_, components * 2)
def test_store_correlations():
from pymks import MKSStructureAnalysis
from pymks import PrimitiveBasis
from pymks.stats import correlate
p_basis = PrimitiveBasis(2)
model = MKSStructureAnalysis(basis=p_basis, store_correlations=True)
X = np.random.randint(2, size=(2, 4, 4))
model.fit(X)
X = correlate(X, p_basis, correlations=[(0, 0), (0, 1)])
assert np.allclose(X, model.fit_correlations)
X_0 = np.random.randint(2, size=(2, 4, 4))
model.transform(X_0)
X_corr_0 = correlate(X_0, p_basis, correlations=[(0, 0), (0, 1)])
assert np.allclose(X_corr_0, model.transform_correlations)
X_1 = np.random.randint(2, size=(2, 4, 4))
model.transform(X_1)
X_corr_1 = correlate(X_1, p_basis, correlations=[(0, 0), (0, 1)])
X_corr_ = np.concatenate((X_corr_0, X_corr_1))
assert np.allclose(X_corr_, model.transform_correlations)
def test_default_dimension_reducer():
from sklearn.decomposition import PCA
from pymks import MKSStructureAnalysis
from pymks import PrimitiveBasis
model = MKSStructureAnalysis(basis=PrimitiveBasis())
assert isinstance(model.dimension_reducer, PCA)
def test_default_n_components():
from pymks import MKSStructureAnalysis
from pymks import DiscreteIndicatorBasis
dbasis = DiscreteIndicatorBasis(n_states=2)
model = MKSStructureAnalysis(basis=dbasis)
assert model.n_components == 5