def test_fista_regression_simplex():
rng = np.random.RandomState(0)
w = project_simplex(rng.rand(10))
X = rng.randn(1000, 10)
y = np.dot(X, w)
reg = FistaRegressor(penalty="simplex", max_iter=100, verbose=0)
reg.fit(X, y)
y_pred = reg.predict(X)
error = np.sqrt(np.mean((y - y_pred) ** 2))
assert_almost_equal(error, 0.000, 3)
assert_true(np.all(reg.coef_ >= 0))
assert_almost_equal(np.sum(reg.coef_), 1.0, 3)
def test_fista_regression_simplex():
rng = np.random.RandomState(0)
w = project_simplex(rng.rand(10))
X = rng.randn(1000, 10)
y = np.dot(X, w)
reg = FistaRegressor(penalty="simplex", max_iter=100, verbose=0)
reg.fit(X, y)
y_pred = reg.predict(X)
error = np.sqrt(np.mean((y - y_pred)**2))
assert_almost_equal(error, 0.000, 3)
assert_true(np.all(reg.coef_ >= 0))
assert_almost_equal(np.sum(reg.coef_), 1.0, 3)
def test_fista_regression_l1_ball():
rng = np.random.RandomState(0)
alpha = 5.0
w = project_simplex(rng.randn(10), alpha)
X = rng.randn(1000, 10)
y = np.dot(X, w)
reg = FistaRegressor(penalty="l1-ball", alpha=alpha, max_iter=100, verbose=0)
reg.fit(X, y)
y_pred = reg.predict(X)
error = np.sqrt(np.mean((y - y_pred) ** 2))
np.testing.assert_almost_equal(error, 0.000, 3)
np.testing.assert_almost_equal(np.sum(np.abs(reg.coef_)), alpha, 3)
def test_fista_regression_l1_ball():
rng = np.random.RandomState(0)
alpha = 5.0
w = project_simplex(rng.randn(10), alpha)
X = rng.randn(1000, 10)
y = np.dot(X, w)
reg = FistaRegressor(penalty="l1-ball", alpha=alpha, max_iter=100, verbose=0)
reg.fit(X, y)
y_pred = reg.predict(X)
error = np.sqrt(np.mean((y - y_pred) ** 2))
assert_almost_equal(error, 0.000, 3)
assert_almost_equal(np.sum(np.abs(reg.coef_)), alpha, 3)
def test_fista_regression_trace():
rng = np.random.RandomState(0)
def _make_data(n_samples, n_features, n_tasks, n_components):
W = rng.rand(n_tasks, n_features) - 0.5
U, S, V = svd(W, full_matrices=True)
S[n_components:] = 0
S = diagsvd(S, U.shape[0], V.shape[0])
W = np.dot(np.dot(U, S), V)
X = rng.rand(n_samples, n_features) - 0.5
Y = np.dot(X, W.T)
return X, Y, W
X, Y, W = _make_data(200, 50,30, 5)
reg = FistaRegressor(max_iter=15, verbose=0)
reg.fit(X, Y)
Y_pred = reg.predict(X)
error = (Y_pred - Y).ravel()
error = np.dot(error, error)
assert_almost_equal(error, 77.45, 2)
def fit(self, X, y):
if self.w is None:
self.w = np.ones(X.shape[1])
if self.lam is None:
lam_max, lam_min = _get_lam_max_min(X, y, self.eps)
self.lambda_path_ = np.logspace(np.log10(lam_max), np.log10(lam_min), self.n_lam)
else:
self.lambda_path_ = [self.lam]
scorer = make_scorer(self.metric)
self.coef_path_, self.model_path_, self.score_path_ = [], [], []
for lam_i in self.lambda_path_:
# Setup model
per_model_n = len(y) * ((self.cv-1) / self.cv)
model_i = FistaRegressor(
C=1/per_model_n,
penalty=_LassoProjection(self.w),
alpha=lam_i
)
# Get fit data
scores_i = cross_val_score(
model_i,
X, y,
scoring=scorer,
cv=self.cv,
n_jobs=self.n_jobs
)
# Fit model
model_i.fit(X, y)
self.coef_path_.append(model_i.coef_)
self.score_path_.append(scores_i.mean())
self.model_path_.append(model_i)
self.coef_path_ = np.vstack(self.coef_path_)
self.best_index = np.argmin(self.score_path_)
def test_fista_regression():
reg = FistaRegressor(max_iter=100, verbose=0)
reg.fit(bin_dense, bin_target)
y_pred = np.sign(reg.predict(bin_dense))
assert_almost_equal(np.mean(bin_target == y_pred), 0.985)
def test_fista_regression():
reg = FistaRegressor(max_iter=100, verbose=0)
reg.fit(bin_dense, bin_target)
y_pred = np.sign(reg.predict(bin_dense))
assert_almost_equal(np.mean(bin_target == y_pred), 0.985)
def test_fista_regression(bin_dense_train_data):
X, y = bin_dense_train_data
reg = FistaRegressor(max_iter=100, verbose=0)
reg.fit(X, y)
y_pred = np.sign(reg.predict(X))
np.testing.assert_almost_equal(np.mean(y == y_pred), 0.985)
