def test_max_lambda_with_weights(self):
'''Test that the calculations of max_lambda inside the fortran code and
inside the python code give the same result on both dense and sparse
matricies, even when sample weights come into play.
'''
Xdn = np.random.random(size=(50, 10))
Xsp = csc_matrix(Xdn)
w = np.random.random(size=(10, ))
y = np.dot(Xdn, w)
sw = np.random.uniform(size=(50, ))
for alpha in [.01, .5, 1]:
for X in (Xdn, Xsp):
enet = ElasticNet(alpha=alpha)
enet.fit(X, y, weights=sw)
ol = enet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1] / ol[2])
max_lambda_from_python = enet._max_lambda(X, y, weights=sw)
self.assertAlmostEqual(max_lambda_from_fortran,
max_lambda_from_python, 4)
def test_max_lambda_with_weights(self):
'''Test that the calculations of max_lambda inside the fortran code and
inside the python code give the same result on both dense and sparse
matricies, even when sample weights come into play.
'''
Xdn = np.random.random(size=(50,10))
Xsp = csc_matrix(Xdn)
w = np.random.random(size=(10,))
y = np.dot(Xdn, w)
sw = np.random.uniform(size=(50,))
for alpha in [.01, .5, 1]:
for X in (Xdn, Xsp):
enet = ElasticNet(alpha=alpha)
enet.fit(X, y, weights=sw)
ol = enet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1]/ol[2])
max_lambda_from_python = enet._max_lambda(X, y, weights=sw)
self.assertAlmostEqual(
max_lambda_from_fortran, max_lambda_from_python, 4
)
def test_max_lambda(self):
'''Test that the calculations of max_lambda inside the fortran code and
inside the python code give the same result on both dense and sparse
matricies.
Note that the implementation of max_lambda for alpha=0 in
the fortran code is unknown, so we currently do not test against it.
'''
Xdn = np.random.random(size=(50, 10))
Xsp = csc_matrix(Xdn)
w = np.random.random(size=(10, ))
y = np.dot(Xdn, w)
for alpha in [.01, .5, 1]:
for X in (Xdn, Xsp):
enet = ElasticNet(alpha=alpha)
enet.fit(X, y)
ol = enet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1] / ol[2])
max_lambda_from_python = enet._max_lambda(X, y)
self.assertAlmostEqual(max_lambda_from_fortran,
max_lambda_from_python, 4)
def test_max_lambda(self):
'''Test that the calculations of max_lambda inside the fortran code and
inside the python code give the same result on both dense and sparse
matricies.
Note that the implementation of max_lambda for alpha=0 in
the fortran code is unknown, so we currently do not test against it.
'''
Xdn = np.random.random(size=(50,10))
Xsp = csc_matrix(Xdn)
w = np.random.random(size=(10,))
y = np.dot(Xdn, w)
for alpha in [.01, .5, 1]:
for X in (Xdn, Xsp):
enet = ElasticNet(alpha=alpha)
enet.fit(X, y)
ol = enet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1]/ol[2])
max_lambda_from_python = enet._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.random(size=(50,10))
w = np.random.random(size=(10,))
y = np.dot(X, w)
# Edge case
# A single lambda is so big that it sets all estimated coefficients
# to zero. This used to break the predict method.
enet = ElasticNet(alpha=1)
enet.fit(X, y, lambdas=[10**5])
_ = enet.predict(X)
# Edge case
# Multiple lambdas are so big as to set all estiamted coefficients
# to zero. This used to break the predict method.
enet = ElasticNet(alpha=1)
enet.fit(X, y, lambdas=[10**5, 2*10**5])
_ = enet.predict(X)
# Edge case:
# Some predictors have zero varaince. This used to break lambda
# max.
X = np.random.random(size=(50,10))
X[:,2] = 0; X[:,8] = 1
y = np.dot(X, w)
enet = ElasticNet(alpha=.1)
enet.fit(X, y)
ol = enet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1]/ol[2])
max_lambda_from_python = enet._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))
enet = ElasticNet(alpha=.1)
enet.fit(X, y)
def test_edge_cases(self):
'''Edge cases in model specification.'''
X = np.random.random(size=(50, 10))
w = np.random.random(size=(10, ))
y = np.dot(X, w)
# Edge case
# A single lambda is so big that it sets all estimated coefficients
# to zero. This used to break the predict method.
enet = ElasticNet(alpha=1)
enet.fit(X, y, lambdas=[10**5])
_ = enet.predict(X)
# Edge case
# Multiple lambdas are so big as to set all estiamted coefficients
# to zero. This used to break the predict method.
enet = ElasticNet(alpha=1)
enet.fit(X, y, lambdas=[10**5, 2 * 10**5])
_ = enet.predict(X)
# Edge case:
# Some predictors have zero varaince. This used to break lambda
# max.
X = np.random.random(size=(50, 10))
X[:, 2] = 0
X[:, 8] = 1
y = np.dot(X, w)
enet = ElasticNet(alpha=.1)
enet.fit(X, y)
ol = enet.out_lambdas
max_lambda_from_fortran = ol[1] * (ol[1] / ol[2])
max_lambda_from_python = enet._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))
enet = ElasticNet(alpha=.1)
enet.fit(X, y)
