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@Jiang-Kangkang
Jiang-Kangkang committed May 16, 2021
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Showing 2 changed files with 76 additions and 76 deletions.
2 changes: 1 addition & 1 deletion .travis.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ matrix:
script:
- ls *
- travis_wait 120 pytest ./ --cov=./ --cov-report=xml
- gcovr ./python/src/ --xml
- gcovr ./python/src/ --exclude-directories=./python/include/* --xml
- ls *

after_success:
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150 changes: 75 additions & 75 deletions python/pytest/test_all.py
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Expand Up @@ -42,44 +42,44 @@ def test_gaussian(self):
assert model.beta[nonzero_fit] == approx(reg.coef_, rel=1e-5, abs=1e-5)
assert (nonzero_true == nonzero_fit).all()

# def test_binomial(self):
# n = 1000
# p = 400
# k = 20
# family = "binomial"
# rho = 0.5
# sigma = 1
# np.random.seed(3)
# data = gen_data(n, p, family=family, k=k, rho=rho, sigma=sigma)
# sequence = range(0, int(n/np.log(np.log(n)) / np.log(p)))
# print("logistic abess")

# # model = abessLogistic(path_type="seq", sequence=sequence, ic_type='ebic', is_screening=False, screening_size=30,
# # K_max=10, epsilon=10, powell_path=2, s_min=1, s_max=p, lambda_min=0.01, lambda_max=100, is_cv=True, K=5,
# # exchange_num=2, tau=0.1 * np.log(n*p) / n,
# # primary_model_fit_max_iter=10, primary_model_fit_epsilon=1e-6, early_stop=False, approximate_Newton=True, ic_coef=1., thread=5)
# # group = np.linspace(1, p, p)
# # model.fit(data.x, data.y, group=group)

# model2 = abessLogistic(path_type="seq", sequence=sequence, ic_type='ebic', is_screening=False, screening_size=30,
# K_max=10, epsilon=10, powell_path=2, s_min=1, s_max=p, lambda_min=0.01, lambda_max=100, is_cv=False, K=5,
# exchange_num=2, tau=0.1 * np.log(n*p) / n,
# primary_model_fit_max_iter=80, primary_model_fit_epsilon=1e-6, early_stop=False, approximate_Newton=False, ic_coef=1., thread=5)
# group = np.linspace(1, p, p)
# model2.fit(data.x, data.y, group=group)
def test_binomial(self):
n = 200
p = 100
k = 20
family = "binomial"
rho = 0.5
sigma = 1
np.random.seed(3)
data = gen_data(n, p, family=family, k=k, rho=rho, sigma=sigma)
sequence = range(0, int(n/np.log(np.log(n)) / np.log(p)))
print("logistic abess")

# model = abessLogistic(path_type="seq", sequence=sequence, ic_type='ebic', is_screening=False, screening_size=30,
# K_max=10, epsilon=10, powell_path=2, s_min=1, s_max=p, lambda_min=0.01, lambda_max=100, is_cv=True, K=5,
# exchange_num=2, tau=0.1 * np.log(n*p) / n,
# primary_model_fit_max_iter=10, primary_model_fit_epsilon=1e-6, early_stop=False, approximate_Newton=True, ic_coef=1., thread=5)
# group = np.linspace(1, p, p)
# model.fit(data.x, data.y, group=group)

model2 = abessLogistic(path_type="seq", sequence=sequence, ic_type='ebic', is_screening=False, screening_size=30,
K_max=10, epsilon=10, powell_path=2, s_min=1, s_max=p, lambda_min=0.01, lambda_max=100, is_cv=False, K=5,
exchange_num=2, tau=0.1 * np.log(n*p) / n,
primary_model_fit_max_iter=80, primary_model_fit_epsilon=1e-6, early_stop=False, approximate_Newton=False, ic_coef=1., thread=1)
group = np.linspace(1, p, p)
model2.fit(data.x, data.y, group=group)

# nonzero_true = np.nonzero(data.beta)[0]
# nonzero_fit = np.nonzero(model2.beta)[0]
# print(nonzero_true)
# print(nonzero_fit)
# # new_x = data.x[:, nonzero_fit]
# # reg = linear_model.LogisticRegression(penalty="none")
# # reg.fit(new_x, data.y)
# # print(model2.beta[nonzero_fit])
# # print(reg.coef_)
# assert (nonzero_true == nonzero_fit).all()
# # assert model2.beta[nonzero_fit] == approx(
# # reg.coef_[0], rel=1e-2, abs=1e-2)
nonzero_true = np.nonzero(data.beta)[0]
nonzero_fit = np.nonzero(model2.beta)[0]
print(nonzero_true)
print(nonzero_fit)
# new_x = data.x[:, nonzero_fit]
# reg = linear_model.LogisticRegression(penalty="none")
# reg.fit(new_x, data.y)
# print(model2.beta[nonzero_fit])
# print(reg.coef_)
# assert (nonzero_true == nonzero_fit).all()
# assert model2.beta[nonzero_fit] == approx(
# reg.coef_[0], rel=1e-2, abs=1e-2)

# def test_cox(self):
# n = 1000
Expand Down Expand Up @@ -132,45 +132,45 @@ def test_gaussian(self):
# # assert model2.beta[nonzero_fit] == approx(
# # cph.params_.values, rel=2e-1, abs=2e-1)

# def test_poisson(self):
# # to do
# n = 1000
# p = 100
# k = 10
# family = "poisson"
# rho = 0.5
# sigma = 1
# np.random.seed(3)
# data = gen_data(n, p, family=family, k=k, rho=rho, sigma=sigma)
# sequence = range(0, 11)

# # model = abessLogistic(path_type="seq", sequence=sequence, ic_type='ebic', is_screening=False, screening_size=30,
# # K_max=10, epsilon=10, powell_path=2, s_min=1, s_max=p, lambda_min=0.01, lambda_max=100, is_cv=True, K=5,
# # exchange_num=2, tau=0.1 * np.log(n*p) / n,
# # primary_model_fit_max_iter=10, primary_model_fit_epsilon=1e-6, early_stop=False, approximate_Newton=True, ic_coef=1., thread=5)
# # group = np.linspace(1, p, p)
# # model.fit(data.x, data.y, group=group)

# model2 = abessLogistic(path_type="seq", sequence=sequence, ic_type='ebic', is_screening=False, screening_size=30,
# K_max=10, epsilon=10, powell_path=2, s_min=1, s_max=p, lambda_min=0.01, lambda_max=100, is_cv=False, K=5,
# exchange_num=2, tau=0.1 * np.log(n*p) / n,
# primary_model_fit_max_iter=80, primary_model_fit_epsilon=1e-6, early_stop=False, approximate_Newton=False, ic_coef=1., thread=5)
# group = np.linspace(1, p, p)
# model2.fit(data.x, data.y, group=group)
def test_poisson(self):
# to do
n = 200
p = 100
k = 10
family = "poisson"
rho = 0.5
sigma = 1
np.random.seed(3)
data = gen_data(n, p, family=family, k=k, rho=rho, sigma=sigma)
sequence = range(0, 11)

# model = abessLogistic(path_type="seq", sequence=sequence, ic_type='ebic', is_screening=False, screening_size=30,
# K_max=10, epsilon=10, powell_path=2, s_min=1, s_max=p, lambda_min=0.01, lambda_max=100, is_cv=True, K=5,
# exchange_num=2, tau=0.1 * np.log(n*p) / n,
# primary_model_fit_max_iter=10, primary_model_fit_epsilon=1e-6, early_stop=False, approximate_Newton=True, ic_coef=1., thread=5)
# group = np.linspace(1, p, p)
# model.fit(data.x, data.y, group=group)

model2 = abessPoisson(path_type="seq", sequence=sequence, ic_type='ebic', is_screening=False, screening_size=30,
K_max=10, epsilon=10, powell_path=2, s_min=1, s_max=p, lambda_min=0.01, lambda_max=100, is_cv=False, K=5,
exchange_num=2, tau=0.1 * np.log(n*p) / n,
primary_model_fit_max_iter=80, primary_model_fit_epsilon=1e-6, early_stop=False, approximate_Newton=False, ic_coef=1., thread=1)
group = np.linspace(1, p, p)
model2.fit(data.x, data.y, group=group)

# nonzero_true = np.nonzero(data.beta)[0]
# nonzero_fit = np.nonzero(model2.beta)[0]
# print(nonzero_true)
# print(nonzero_fit)
# # new_x = data.x[:, nonzero_fit]
# # reg = linear_model.LogisticRegression(
# # penalty="none", tol=1e-6, max_iter=200)
# # reg.fit(new_x, data.y)
# # print(model2.beta[nonzero_fit])
# # print(reg.coef_)
# assert (nonzero_true == nonzero_fit).all()
# # assert model2.beta[nonzero_fit] == approx(
# # reg.coef_[0], rel=1e-2, abs=1e-2)
nonzero_true = np.nonzero(data.beta)[0]
nonzero_fit = np.nonzero(model2.beta)[0]
print(nonzero_true)
print(nonzero_fit)
# new_x = data.x[:, nonzero_fit]
# reg = linear_model.LogisticRegression(
# penalty="none", tol=1e-6, max_iter=200)
# reg.fit(new_x, data.y)
# print(model2.beta[nonzero_fit])
# print(reg.coef_)
# assert (nonzero_true == nonzero_fit).all()
# assert model2.beta[nonzero_fit] == approx(
# reg.coef_[0], rel=1e-2, abs=1e-2)

# def test_mulgaussian(self):
# n = 1000
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