def test_dict_fact_normalize():
# Generate some toy data.
rng = np.random.RandomState(0)
U = rng.rand(50, 3)
V = rng.rand(3, 20)
X = np.dot(U, V)
mf = DictMF(n_components=3,
n_epochs=1,
alpha=1e-3,
random_state=0,
verbose=0,
normalize=True)
mf.fit(X)
Y = np.dot(mf.P_.T, mf.Q_)
Y += mf.col_mean_[np.newaxis, :]
Y += mf.row_mean_[:, np.newaxis]
Y2 = mf.predict(X).toarray()
assert_array_almost_equal(Y, Y2)
rmse = np.sqrt(np.mean((X - Y)**2))
rmse2 = mf.score(X)
assert_almost_equal(rmse, rmse2)
def test_dummy_axis1():
est = Dummy()
est.fit(X)
X_predicted = est.predict(X).toarray()
X_expected = [[2, 0, 0, 2], [3.5, 0, 3.5, 0], [0, 3.5, 3.5, 0],
[0, 0, 2, 0], [1, 0, 0, 0]]
assert_array_almost_equal(X_predicted, X_expected)
def test_matrix_fact_cd():
# Generate some toy data.
rng = np.random.RandomState(0)
U = rng.rand(50, 3)
V = rng.rand(3, 20)
X = np.dot(U, V)
mf = DictMF(n_components=3,
n_epochs=3,
alpha=1e-3,
random_state=0,
verbose=0,
normalize=False)
mf.fit(X)
Y = np.dot(mf.P_.T, mf.Q_)
Y2 = mf.predict(X).toarray()
assert_array_almost_equal(Y, Y2)
rmse = np.sqrt(np.mean((X - Y)**2))
rmse2 = mf.score(X)
assert_almost_equal(rmse, rmse2)
def test_dummy_axis0():
est = Dummy(axis=0)
est.fit(X)
X_predicted = est.predict(X).toarray()
X_expected = [[2, 0, 0, 1], [2, 0, 10. / 3, 0], [0, 4, 10. / 3, 0],
[0, 0, 10. / 3, 0], [2, 0, 0, 0]]
assert_array_almost_equal(X_predicted, X_expected)
def test_dummy_axis1():
est = Dummy()
est.fit(X)
X_predicted = est.predict(X).toarray()
X_expected = [[2, 0, 0, 2],
[3.5, 0, 3.5, 0],
[0, 3.5, 3.5, 0],
[0, 0, 2, 0],
[1, 0, 0, 0]]
assert_array_almost_equal(X_predicted, X_expected)
def test_dummy_axis0():
est = Dummy(axis=0)
est.fit(X)
X_predicted = est.predict(X).toarray()
X_expected = [[2, 0, 0, 1],
[2, 0, 10./3, 0],
[0, 4, 10./3, 0],
[0, 0, 10./3, 0],
[2, 0, 0, 0]]
assert_array_almost_equal(X_predicted, X_expected)
def test_center_std_axis0():
X_t = X.copy()
mean = _mean_axis0(X)
std = _std_axis0(X)
for i in xrange(X.shape[0]):
for j in xrange(X.shape[1]):
if X[i, j] == 0:
continue
X_t[i, j] -= mean[j]
X_t[i, j] /= std[j]
scaler = StandardScaler(axis=0, with_std=True)
X_t2 = scaler.fit_transform(X)
assert_array_almost_equal(X_t, X_t2.toarray())
X2 = scaler.inverse_transform(X_t2)
assert_array_almost_equal(X, X2.toarray())
def test_center_std_axis0():
X_t = X.copy()
mean = _mean_axis0(X)
std = _std_axis0(X)
for i in xrange(X.shape[0]):
for j in xrange(X.shape[1]):
if X[i, j] == 0:
continue
X_t[i, j] -= mean[j]
X_t[i, j] /= std[j]
scaler = StandardScaler(axis=0, with_std=True)
X_t2 = scaler.fit_transform(X)
assert_array_almost_equal(X_t, X_t2.toarray())
X2 = scaler.inverse_transform(X_t2)
assert_array_almost_equal(X, X2.toarray())
def test_matrix_fact_cd():
# Generate some toy data.
rng = np.random.RandomState(0)
U = rng.rand(50, 3)
V = rng.rand(3, 20)
X = np.dot(U, V)
mf = ExplicitMF(n_components=3, max_iter=10, alpha=1e-3, random_state=0,
verbose=0)
mf.fit(X)
Y = np.dot(mf.P_, mf.Q_)
Y2 = mf.predict(X).toarray()
assert_array_almost_equal(Y, Y2)
rmse = np.sqrt(np.mean((X - Y) ** 2))
rmse2 = mf.score(X)
assert_almost_equal(rmse, rmse2)
def test_dict_fact_normalize():
# Generate some toy data.
rng = np.random.RandomState(0)
U = rng.rand(50, 3)
V = rng.rand(3, 20)
X = np.dot(U, V)
mf = DictMF(n_components=3, n_epochs=1, alpha=1e-3, random_state=0,
verbose=0, normalize=True)
mf.fit(X)
Y = np.dot(mf.P_.T, mf.Q_)
Y += mf.col_mean_[np.newaxis, :]
Y += mf.row_mean_[:, np.newaxis]
Y2 = mf.predict(X).toarray()
assert_array_almost_equal(Y, Y2)
rmse = np.sqrt(np.mean((X - Y) ** 2))
rmse2 = mf.score(X)
assert_almost_equal(rmse, rmse2)
def test_mean_axis0():
expected = [2, 4, 10. / 3, 1]
for func in (sp.csr_matrix, sp.csc_matrix, sp.coo_matrix):
means = mean(func(X, dtype=np.float64), axis=0)
assert_array_almost_equal(means, expected)
def test_mean_axis1():
expected = [2, 3.5, 3.5, 2, 1]
for func in (sp.csr_matrix, sp.csc_matrix, sp.coo_matrix):
means = mean(func(X, dtype=np.float64))
assert_array_almost_equal(means, expected)
def test_mean_axis0():
expected = [2, 4, 10./3, 1]
for func in (sp.csr_matrix, sp.csc_matrix, sp.coo_matrix):
means = mean(func(X, dtype=np.float64), axis=0)
assert_array_almost_equal(means, expected)
def test_mean_axis1():
expected = [2, 3.5, 3.5, 2, 1]
for func in (sp.csr_matrix, sp.csc_matrix, sp.coo_matrix):
means = mean(func(X, dtype=np.float64))
assert_array_almost_equal(means, expected)
