def test_auto_low_rank():
"""Test probabilistic low rank estimators"""
n_samples, n_features, rank = 400, 20, 10
sigma = 0.1
def get_data(n_samples, n_features, rank, sigma):
rng = np.random.RandomState(42)
W = rng.randn(n_features, n_features)
X = rng.randn(n_samples, rank)
U, _, _ = linalg.svd(W.copy())
X = np.dot(X, U[:, :rank].T)
sigmas = sigma * rng.rand(n_features) + sigma / 2.
X += rng.randn(n_samples, n_features) * sigmas
return X
X = get_data(n_samples=n_samples,
n_features=n_features,
rank=rank,
sigma=sigma)
method_params = {'iter_n_components': [9, 10, 11]}
cv = 3
n_jobs = 1
mode = 'factor_analysis'
rescale = 1e8
X *= rescale
est, info = _auto_low_rank_model(X,
mode=mode,
n_jobs=n_jobs,
method_params=method_params,
cv=cv)
assert_equal(info['best'], rank)
X = get_data(n_samples=n_samples,
n_features=n_features,
rank=rank,
sigma=sigma)
method_params = {'iter_n_components': [n_features + 5]}
msg = ('You are trying to estimate %i components on matrix '
'with %i features.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
_auto_low_rank_model(X,
mode=mode,
n_jobs=n_jobs,
method_params=method_params,
cv=cv)
assert_equal(len(w), 1)
assert_equal(msg % (n_features + 5, n_features), '%s' % w[0].message)
method_params = {'iter_n_components': [n_features + 5]}
assert_raises(ValueError,
_auto_low_rank_model,
X,
mode='foo',
n_jobs=n_jobs,
method_params=method_params,
cv=cv)
def test_auto_low_rank():
"""Test probabilistic low rank estimators."""
n_samples, n_features, rank = 400, 10, 5
sigma = 0.1
def get_data(n_samples, n_features, rank, sigma):
rng = np.random.RandomState(42)
W = rng.randn(n_features, n_features)
X = rng.randn(n_samples, rank)
U, _, _ = linalg.svd(W.copy())
X = np.dot(X, U[:, :rank].T)
sigmas = sigma * rng.rand(n_features) + sigma / 2.
X += rng.randn(n_samples, n_features) * sigmas
return X
X = get_data(n_samples=n_samples,
n_features=n_features,
rank=rank,
sigma=sigma)
method_params = {'iter_n_components': [4, 5, 6]}
cv = 3
n_jobs = 1
mode = 'factor_analysis'
rescale = 1e8
X *= rescale
est, info = _auto_low_rank_model(X,
mode=mode,
n_jobs=n_jobs,
method_params=method_params,
cv=cv)
assert_equal(info['best'], rank)
X = get_data(n_samples=n_samples,
n_features=n_features,
rank=rank,
sigma=sigma)
method_params = {'iter_n_components': [n_features + 5]}
msg = ('You are trying to estimate %i components on matrix '
'with %i features.') % (n_features + 5, n_features)
with pytest.warns(RuntimeWarning, match=msg):
_auto_low_rank_model(X,
mode=mode,
n_jobs=n_jobs,
method_params=method_params,
cv=cv)
method_params = {'iter_n_components': [n_features + 5]}
pytest.raises(ValueError,
_auto_low_rank_model,
X,
mode='foo',
n_jobs=n_jobs,
method_params=method_params,
cv=cv)
def test_auto_low_rank():
"""Test probabilistic low rank estimators"""
n_samples, n_features, rank = 400, 20, 10
sigma = 0.1
def get_data(n_samples, n_features, rank, sigma):
rng = np.random.RandomState(42)
W = rng.randn(n_features, n_features)
X = rng.randn(n_samples, rank)
U, _, _ = linalg.svd(W.copy())
X = np.dot(X, U[:, :rank].T)
sigmas = sigma * rng.rand(n_features) + sigma / 2.
X += rng.randn(n_samples, n_features) * sigmas
return X
X = get_data(n_samples=n_samples, n_features=n_features, rank=rank,
sigma=sigma)
method_params = {'iter_n_components': [9, 10, 11]}
cv = 3
n_jobs = 1
mode = 'factor_analysis'
rescale = 1e8
X *= rescale
est, info = _auto_low_rank_model(X, mode=mode, n_jobs=n_jobs,
method_params=method_params,
cv=cv)
assert_equal(info['best'], rank)
X = get_data(n_samples=n_samples, n_features=n_features, rank=rank,
sigma=sigma)
method_params = {'iter_n_components': [n_features + 5]}
msg = ('You are trying to estimate %i components on matrix '
'with %i features.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
_auto_low_rank_model(X, mode=mode, n_jobs=n_jobs,
method_params=method_params, cv=cv)
assert_equal(len(w), 1)
assert_equal(msg % (n_features + 5, n_features), '%s' % w[0].message)
method_params = {'iter_n_components': [n_features + 5]}
assert_raises(ValueError, _auto_low_rank_model, X, mode='foo',
n_jobs=n_jobs, method_params=method_params, cv=cv)
def test_auto_low_rank():
"""Test probabilistic low rank estimators."""
n_samples, n_features, rank = 400, 10, 5
sigma = 0.1
def get_data(n_samples, n_features, rank, sigma):
rng = np.random.RandomState(42)
W = rng.randn(n_features, n_features)
X = rng.randn(n_samples, rank)
U, _, _ = linalg.svd(W.copy())
X = np.dot(X, U[:, :rank].T)
sigmas = sigma * rng.rand(n_features) + sigma / 2.
X += rng.randn(n_samples, n_features) * sigmas
return X
X = get_data(n_samples=n_samples, n_features=n_features, rank=rank,
sigma=sigma)
method_params = {'iter_n_components': [4, 5, 6]}
cv = 3
n_jobs = 1
mode = 'factor_analysis'
rescale = 1e8
X *= rescale
est, info = _auto_low_rank_model(X, mode=mode, n_jobs=n_jobs,
method_params=method_params,
cv=cv)
assert_equal(info['best'], rank)
X = get_data(n_samples=n_samples, n_features=n_features, rank=rank,
sigma=sigma)
method_params = {'iter_n_components': [n_features + 5]}
msg = ('You are trying to estimate %i components on matrix '
'with %i features.') % (n_features + 5, n_features)
with pytest.warns(RuntimeWarning, match=msg):
_auto_low_rank_model(X, mode=mode, n_jobs=n_jobs,
method_params=method_params, cv=cv)
