def test_max_lambda(self):
Xdn = np.random.uniform(-1, 1, size=(50, 10))
Xsp = csc_matrix(Xdn)
w = np.random.uniform(-1, 1, size=(10, ))
y = (np.dot(Xdn, w) >= 0).astype(int)
for X in (Xdn, Xsp):
for alpha in [.01, .5, 1]:
lnet = LogisticNet(alpha=alpha)
lnet.fit(X, y)
ol = lnet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1] / ol[2])
max_lambda_from_python = lnet._max_lambda(X, y)
self.assertAlmostEqual(max_lambda_from_fortran,
max_lambda_from_python, 4)
def test_max_lambda(self):
Xdn = np.random.uniform(-1, 1, size=(50,10))
Xsp = csc_matrix(Xdn)
w = np.random.uniform(-1, 1, size=(10,))
y = (np.dot(Xdn, w) >= 0).astype(int)
for X in (Xdn, Xsp):
for alpha in [.01, .5, 1]:
lnet = LogisticNet(alpha=alpha)
lnet.fit(X, y)
ol = lnet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1]/ol[2])
max_lambda_from_python = lnet._max_lambda(X, y)
self.assertAlmostEqual(
max_lambda_from_fortran, max_lambda_from_python, 4
)
def test_edge_cases(self):
'''Edge cases in model specification.'''
X = np.random.uniform(-1, 1, size=(50,10))
w = np.random.uniform(-1, 1, size=(10,))
y = (np.dot(X, w) >= 0).astype(int)
# Edge case
# A single lambda is so big that it sets all estimated coefficients
# to zero. This used to break the predict method.
lnet = LogisticNet(alpha=1)
lnet.fit(X, y, lambdas=[10**5])
_ = lnet.predict(X)
# Edge case
# Multiple lambdas are so big as to set all estiamted coefficients
# to zero. This used to break the predict method.
lnet = LogisticNet(alpha=1)
lnet.fit(X, y, lambdas=[10**5, 2*10**5])
_ = lnet.predict(X)
# Edge case:
# Some predictors have zero varaince. This used to break lambda
# max.
X = np.random.uniform(-1, 1, size=(50,10))
X[2,:] = 0; X[8,:] = 0
y = (np.dot(X, w) >= 0).astype(int)
lnet = LogisticNet(alpha=.1)
lnet.fit(X, y)
ol = lnet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1]/ol[2])
max_lambda_from_python = lnet._max_lambda(X, y)
self.assertAlmostEqual(
max_lambda_from_fortran, max_lambda_from_python, 4
)
# Edge case.
# All predictors have zero variance. This is an error in
# sepcification.
with self.assertRaises(ValueError):
X = np.ones(shape=(50,10))
lnet = LogisticNet(alpha=.1)
lnet.fit(X, y)
def test_edge_cases(self):
'''Edge cases in model specification.'''
X = np.random.uniform(-1, 1, size=(50, 10))
w = np.random.uniform(-1, 1, size=(10, ))
y = (np.dot(X, w) >= 0).astype(int)
# Edge case
# A single lambda is so big that it sets all estimated coefficients
# to zero. This used to break the predict method.
lnet = LogisticNet(alpha=1)
lnet.fit(X, y, lambdas=[10**5])
_ = lnet.predict(X)
# Edge case
# Multiple lambdas are so big as to set all estiamted coefficients
# to zero. This used to break the predict method.
lnet = LogisticNet(alpha=1)
lnet.fit(X, y, lambdas=[10**5, 2 * 10**5])
_ = lnet.predict(X)
# Edge case:
# Some predictors have zero varaince. This used to break lambda
# max.
X = np.random.uniform(-1, 1, size=(50, 10))
X[2, :] = 0
X[8, :] = 0
y = (np.dot(X, w) >= 0).astype(int)
lnet = LogisticNet(alpha=.1)
lnet.fit(X, y)
ol = lnet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1] / ol[2])
max_lambda_from_python = lnet._max_lambda(X, y)
self.assertAlmostEqual(max_lambda_from_fortran, max_lambda_from_python,
4)
# Edge case.
# All predictors have zero variance. This is an error in
# sepcification.
with self.assertRaises(ValueError):
X = np.ones(shape=(50, 10))
lnet = LogisticNet(alpha=.1)
lnet.fit(X, y)