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_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_grid_search():
cv = ShuffleSplit(n_iter=5, random_state=0)
mf = ExplicitMF(n_components=3, max_iter=10, random_state=0)
param_grid = {"alpha": [0.1, 1.0, 10]}
gcv = GridSearchCV(mf, param_grid, cv)
gcv.fit(X)
assert_equal(gcv.best_estimator_.alpha, 0.1)
assert_equal(gcv.best_params_, {"alpha": 0.1})
mf = ExplicitMF(alpha=0.1, n_components=3, max_iter=10, random_state=0)
mf.fit(X)
assert_almost_equal(mf.score(X), gcv.score(X))
def test_grid_search():
cv = ShuffleSplit(n_iter=5, random_state=0)
mf = ExplicitMF(n_components=3, max_iter=10, random_state=0)
param_grid = {"alpha": [0.1, 1.0, 10]}
gcv = GridSearchCV(mf, param_grid, cv)
gcv.fit(X)
assert_equal(gcv.best_estimator_.alpha, 0.1)
assert_equal(gcv.best_params_, {"alpha": 0.1})
mf = ExplicitMF(alpha=0.1, n_components=3, max_iter=10, random_state=0)
mf.fit(X)
assert_almost_equal(mf.score(X), gcv.score(X))
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)
