def test_lasso_path_return_models_vs_new_return_gives_same_coefficients():
# Test that lasso_path with lars_path style output gives the
# same result
# Some toy data
X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
y = np.array([1, 2, 3.1])
alphas = [5., 1., .5]
# Compute the lasso_path
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
coef_path = [e.coef_ for e in lasso_path(X, y, alphas=alphas,
fit_intercept=False)]
# Use lars_path and lasso_path(new output) with 1D linear interpolation
# to compute the the same path
alphas_lars, _, coef_path_lars = lars_path(X, y, method='lasso')
coef_path_cont_lars = interpolate.interp1d(alphas_lars[::-1],
coef_path_lars[:, ::-1])
alphas_lasso2, coef_path_lasso2, _ = lasso_path(X, y, alphas=alphas,
fit_intercept=False,
return_models=False)
coef_path_cont_lasso = interpolate.interp1d(alphas_lasso2[::-1],
coef_path_lasso2[:, ::-1])
np.testing.assert_array_almost_equal(coef_path_cont_lasso(alphas),
np.asarray(coef_path).T, decimal=1)
np.testing.assert_array_almost_equal(coef_path_cont_lasso(alphas),
coef_path_cont_lars(alphas),
decimal=1)
def test_lasso_path_return_models_vs_new_return_gives_same_coefficients():
# Test that lasso_path with lars_path style output gives the
# same result
# Some toy data
X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
y = np.array([1, 2, 3.1])
alphas = [5., 1., .5]
# Compute the lasso_path
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
coef_path = [
e.coef_ for e in lasso_path(
X, y, alphas=alphas, return_models=True, fit_intercept=False)
]
# Use lars_path and lasso_path(new output) with 1D linear interpolation
# to compute the the same path
alphas_lars, _, coef_path_lars = lars_path(X, y, method='lasso')
coef_path_cont_lars = interpolate.interp1d(alphas_lars[::-1],
coef_path_lars[:, ::-1])
alphas_lasso2, coef_path_lasso2, _ = lasso_path(X,
y,
alphas=alphas,
fit_intercept=False,
return_models=False)
coef_path_cont_lasso = interpolate.interp1d(alphas_lasso2[::-1],
coef_path_lasso2[:, ::-1])
np.testing.assert_array_almost_equal(coef_path_cont_lasso(alphas),
np.asarray(coef_path).T,
decimal=1)
np.testing.assert_array_almost_equal(coef_path_cont_lasso(alphas),
coef_path_cont_lars(alphas),
decimal=1)
def test_ElasticnetWeights():
"""Test elastic net with different weight for each predictor
alpha: a vector of weight, small # means prior knowledge
1 : means no prior knowledge
"""
# Has 10 features
diabetes = datasets.load_diabetes()
# pprint(diabetes)
print("Size of data:{}".format(diabetes.data.shape))
X = diabetes.data
y = diabetes.target
X /= X.std(axis=0) # Standardize data (easier to set the l1_ratio parameter)
eps = 5e-3 # the smaller it is the longer is the path
alphas = np.arange(2, 4, 0.2)
alphas = np.append(alphas, 2.27889) # best aplpha from cv
# Computing regularization path using the lasso
alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps, fit_intercept=False,
alphas=alphas)
# Computing regularization path using the elastic net
alphas_enet, coefs_enet, _ = enet_path(
X, y, eps=eps, l1_ratio=0.8, fit_intercept=False, alphas=alphas)
# ElasticnetCV
num_predict = X.shape[1]
alphas = np.zeros(num_predict)
alphas.fill(1)
val = 0.1
alphas[2] = val
alphas[3] = val
alphas[6] = val
enetCV_alpha, enetCV_coef = runPrintResults(X,y, None, "EnetCV")
runPrintResults(X,y, alphas, "EnetCVWeight 1")
# print("coefs_enet: {}".format(coefs_enet[:, -1]))
# print("coefs_lasso: {}".format(coefs_lasso[:, -1]))
# Display results
plt.figure(1)
ax = plt.gca()
ax.set_color_cycle(2 * ['b', 'r', 'g', 'c', 'k'])
l1 = plt.plot(alphas_lasso, coefs_lasso.T)
l2 = plt.plot(alphas_enet, coefs_enet.T, linestyle='--')
# repeat alpha for x-axis values for plotting
enetCV_alphaVect = [enetCV_alpha] * num_predict
l3 = plt.scatter(enetCV_alphaVect, enetCV_coef, marker='x')
plt.xlabel('alpha')
plt.ylabel('coefficients')
plt.title('Lasso and Elastic-Net Paths')
plt.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'),
loc='upper right')
plt.axis('tight')
plt.savefig("fig/lassoEnet")
def test_lasso_path_return_models_vs_new_return_gives_same_coefficients():
# Test that lasso_path with lars_path style output gives the
# same result
# Some toy data
X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
y = np.array([1, 2, 3.1])
alphas = [5.0, 1.0, 0.5]
# Use lars_path and lasso_path(new output) with 1D linear interpolation
# to compute the same path
alphas_lars, _, coef_path_lars = lars_path(X, y, method="lasso")
coef_path_cont_lars = interpolate.interp1d(alphas_lars[::-1], coef_path_lars[:, ::-1])
alphas_lasso2, coef_path_lasso2, _ = lasso_path(X, y, alphas=alphas, return_models=False)
coef_path_cont_lasso = interpolate.interp1d(alphas_lasso2[::-1], coef_path_lasso2[:, ::-1])
assert_array_almost_equal(coef_path_cont_lasso(alphas), coef_path_cont_lars(alphas), decimal=1)
def test_lasso_path_return_models_vs_new_return_gives_same_coefficients():
# Test that lasso_path with lars_path style output gives the
# same result
# Some toy data
X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
y = np.array([1, 2, 3.1])
alphas = [5., 1., .5]
# Use lars_path and lasso_path(new output) with 1D linear interpolation
# to compute the same path
alphas_lars, _, coef_path_lars = lars_path(X, y, method='lasso')
coef_path_cont_lars = interpolate.interp1d(alphas_lars[::-1],
coef_path_lars[:, ::-1])
alphas_lasso2, coef_path_lasso2, _ = lasso_path(X, y, alphas=alphas,
return_models=False)
coef_path_cont_lasso = interpolate.interp1d(alphas_lasso2[::-1],
coef_path_lasso2[:, ::-1])
assert_array_almost_equal(
coef_path_cont_lasso(alphas), coef_path_cont_lars(alphas),
decimal=1)
def test_ElasticnetWeights():
"""Test elastic net with different weight for each predictor
alpha: a vector of weight, small # means prior knowledge
1 : means no prior knowledge
"""
# Has 10 features
diabetes = datasets.load_diabetes()
# pprint(diabetes)
print("Size of data:{}".format(diabetes.data.shape))
X = diabetes.data
y = diabetes.target
X /= X.std(
axis=0) # Standardize data (easier to set the l1_ratio parameter)
eps = 5e-3 # the smaller it is the longer is the path
alphas = np.arange(2, 4, 0.2)
alphas = np.append(alphas, 2.27889) # best aplpha from cv
# Computing regularization path using the lasso
alphas_lasso, coefs_lasso, _ = lasso_path(X,
y,
eps,
fit_intercept=False,
alphas=alphas)
# Computing regularization path using the elastic net
alphas_enet, coefs_enet, _ = enet_path(X,
y,
eps=eps,
l1_ratio=0.8,
fit_intercept=False,
alphas=alphas)
# ElasticnetCV
num_predict = X.shape[1]
alphas = np.zeros(num_predict)
alphas.fill(1)
val = 0.1
alphas[2] = val
alphas[3] = val
alphas[6] = val
enetCV_alpha, enetCV_coef = runPrintResults(X, y, None, "EnetCV")
runPrintResults(X, y, alphas, "EnetCVWeight 1")
# print("coefs_enet: {}".format(coefs_enet[:, -1]))
# print("coefs_lasso: {}".format(coefs_lasso[:, -1]))
# Display results
plt.figure(1)
ax = plt.gca()
ax.set_color_cycle(2 * ['b', 'r', 'g', 'c', 'k'])
l1 = plt.plot(alphas_lasso, coefs_lasso.T)
l2 = plt.plot(alphas_enet, coefs_enet.T, linestyle='--')
# repeat alpha for x-axis values for plotting
enetCV_alphaVect = [enetCV_alpha] * num_predict
l3 = plt.scatter(enetCV_alphaVect, enetCV_coef, marker='x')
plt.xlabel('alpha')
plt.ylabel('coefficients')
plt.title('Lasso and Elastic-Net Paths')
plt.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='upper right')
plt.axis('tight')
plt.savefig("fig/lassoEnet")
