def test_probabilistic_pca_2():
"""Test that probabilistic PCA correctly separated different datasets"""
n, p = 100, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
ppca = ProbabilisticPCA(n_components=2)
ppca.fit(X)
ll1 = ppca.score(X)
ll2 = ppca.score(rng.randn(n, p) * .2 + np.array([3, 4, 5]))
assert_greater(ll1.mean(), ll2.mean())
def test_probabilistic_pca_2():
"""Test that probabilistic PCA correctly separated different datasets"""
n, p = 100, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
ppca = ProbabilisticPCA(n_components=2)
ppca.fit(X)
ll1 = ppca.score(X)
ll2 = ppca.score(rng.randn(n, p) * .2 + np.array([3, 4, 5]))
assert_greater(ll1.mean(), ll2.mean())
def test_probabilistic_pca_3():
"""The homoscedastic model should work slightly worse
than the heteroscedastic one in over-fitting condition
"""
n, p = 100, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
ppca = ProbabilisticPCA(n_components=2)
ppca.fit(X)
ll1 = ppca.score(X)
ppca.fit(X, homoscedastic=False)
ll2 = ppca.score(X)
def test_probabilistic_pca_3():
"""The homoscedastic model should work slightly worse
than the heteroscedastic one in over-fitting condition
"""
n, p = 100, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
ppca = ProbabilisticPCA(n_components=2)
ppca.fit(X)
ll1 = ppca.score(X)
ppca.fit(X, homoscedastic=False)
ll2 = ppca.score(X)
def test_probabilistic_pca_vs_pca():
"""Test that PCA matches ProbabilisticPCA with homoscedastic=True
"""
n, p = 100, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
pca = PCA(n_components=2).fit(X)
ppca = ProbabilisticPCA(n_components=2).fit(X)
assert_array_almost_equal(pca.score_samples(X), ppca.score(X))
def test_probabilistic_pca_vs_pca():
"""Test that PCA matches ProbabilisticPCA with homoscedastic=True
"""
n, p = 100, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
pca = PCA(n_components=2).fit(X)
ppca = ProbabilisticPCA(n_components=2).fit(X)
assert_array_almost_equal(pca.score_samples(X), ppca.score(X))
def test_probabilistic_pca_1():
"""Test that probabilistic PCA yields a reasonable score"""
n, p = 1000, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
ppca = ProbabilisticPCA(n_components=2)
ppca.fit(X)
ll1 = ppca.score(X)
h = -0.5 * np.log(2 * np.pi * np.exp(1) * 0.1 ** 2) * p
np.testing.assert_almost_equal(ll1.mean() / h, 1, 0)
def test_probabilistic_pca_1():
"""Test that probabilistic PCA yields a reasonable score"""
n, p = 1000, 3
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
ppca = ProbabilisticPCA(n_components=2)
ppca.fit(X)
ll1 = ppca.score(X)
h = -0.5 * np.log(2 * np.pi * np.exp(1) * 0.1**2) * p
np.testing.assert_almost_equal(ll1.mean() / h, 1, 0)
def test_probabilistic_pca_4():
"""Check that ppca select the right model"""
n, p = 200, 3
rng = np.random.RandomState(0)
Xl = rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5]) + np.array([1, 0, 7])
Xt = rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5]) + np.array([1, 0, 7])
ll = np.zeros(p)
for k in range(p):
ppca = ProbabilisticPCA(n_components=k)
ppca.fit(Xl)
ll[k] = ppca.score(Xt).mean()
assert_true(ll.argmax() == 1)
def test_probabilistic_pca_4():
"""Check that ppca select the right model"""
n, p = 200, 3
rng = np.random.RandomState(0)
Xl = (rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5]) +
np.array([1, 0, 7]))
Xt = (rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5]) +
np.array([1, 0, 7]))
ll = np.zeros(p)
for k in range(p):
ppca = ProbabilisticPCA(n_components=k)
ppca.fit(Xl)
ll[k] = ppca.score(Xt).mean()
assert_true(ll.argmax() == 1)
