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 call(n_components, alpha, learning_rate, batch_size):
cb = Callback(X_tr, X_te)
mf = DictMF(n_components=n_components, n_epochs=8, alpha=alpha, verbose=10,
batch_size=batch_size,
normalize=True,
fit_intercept=True,
random_state=0,
learning_rate=learning_rate)
start_time = time.time()
mf.fit(X_tr)
runtime = time.time() - start_time
X_pred = mf.predict(X_te)
rmse = np.sqrt(np.mean((X_pred.data - X_te.data) ** 2))
print('rmse %i %.2f %.2f %.2f: %.4f' % (n_components,
alpha, learning_rate,
batch_size, rmse))
return [n_components, alpha, learning_rate, batch_size, runtime, rmse]
def call(n_components, alpha, learning_rate, batch_size):
cb = Callback(X_tr, X_te)
mf = DictMF(n_components=n_components,
n_epochs=8,
alpha=alpha,
verbose=10,
batch_size=batch_size,
normalize=True,
fit_intercept=True,
random_state=0,
learning_rate=learning_rate)
start_time = time.time()
mf.fit(X_tr)
runtime = time.time() - start_time
X_pred = mf.predict(X_te)
rmse = np.sqrt(np.mean((X_pred.data - X_te.data)**2))
print('rmse %i %.2f %.2f %.2f: %.4f' %
(n_components, alpha, learning_rate, batch_size, rmse))
return [n_components, alpha, learning_rate, batch_size, runtime, rmse]
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_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)
