def setUp(self):
"""Setup simple problem"""
np.random.seed(1)
# This needs to work for different
p = 50
n = 200
F = np.random.randn(n, p)
D = np.diag(np.random.rand(n) * np.sqrt(p))
Sigma = F.dot(F.T) + D
gamma = 1.0
mu = cp.Parameter(n, name='mu')
x = cp.Variable(n)
cost = -mu @ x + gamma * cp.quad_form(x, Sigma) + .5 * cp.norm(x, 1)
constraints = [cp.sum(x) == 1, x >= 0]
problem = cp.Problem(cp.Minimize(cost), constraints)
# Define optimizer
# Force mosek to be single threaded
m = mlopt.Optimizer(problem,
# log_level=logging.DEBUG,
)
'''
Sample points
'''
theta_bar = 10 * np.random.rand(n)
radius = 0.8
'''
Train and solve
'''
# Training and testing data
n_train = 100
n_test = 10
# Sample points from multivariate ball
X_d = uniform_sphere_sample(theta_bar, radius, n=n_train)
self.df_train = pd.DataFrame({'mu': list(X_d)})
# # Train and test using pytorch
# params = {
# 'learning_rate': [0.01],
# 'batch_size': [100],
# 'n_epochs': [200]
# }
#
# m.train(df, parallel=False, learner=mlopt.PYTORCH, params=params)
# Testing data
X_d_test = uniform_sphere_sample(theta_bar, radius, n=n_test)
df_test = pd.DataFrame({'mu': list(X_d_test)})
# Store stuff
self.m = m
self.df_test = df_test
def setUp(self):
"""Setup simple problem"""
np.random.seed(1)
# This needs to work for different
p = 10
n = 30
F = np.random.randn(n, p)
D = np.diag(np.random.rand(n) * np.sqrt(p))
Sigma = F.dot(F.T) + D
gamma = 1.0
mu = cp.Parameter(n, name='mu')
x = cp.Variable(n)
cost = -mu @ x + gamma * cp.quad_form(x, Sigma) + .5 * cp.norm(x, 1)
constraints = [cp.sum(x) == 1, x >= 0]
problem = cp.Problem(cp.Minimize(cost), constraints)
# Define optimizer
m = mlopt.Optimizer(problem,
# log_level=logging.DEBUG
)
'''
Sample points
'''
theta_bar = 10 * np.random.rand(n)
radius = 1.0
'''
Train and solve
'''
# Training and testing data
n_train = 300
n_test = 10
# Sample points from multivariate ball
X_d = uniform_sphere_sample(theta_bar, radius, n=n_train)
df = pd.DataFrame({'mu': list(X_d)})
m.train(df,
filter_strategies=True,
parallel=False,
learner=mlopt.PYTORCH,
n_train_trials=10)
# m.train(df, parallel=False, learner=mlopt.XGBOOST, n_train_trials=10)
# Testing data
X_d_test = uniform_sphere_sample(theta_bar, radius, n=n_test)
df_test = pd.DataFrame({'mu': list(X_d_test)})
# Store stuff
self.m = m
self.df_test = df_test
def test_parameters_in_vectors(self):
"""Check if parameters not in matrices are recognized
"""
# Problem data.
m = 2
n = 1
np.random.seed(1)
A = np.random.randn(m, n)
b = np.random.randn(m)
theta = cp.Parameter(nonneg=True)
theta.value = 8.9
x = cp.Variable(n)
objective = cp.Minimize(cp.sum_squares(A @ x - b) + cp.norm(x, 1))
constraints = [0 <= x, x <= theta]
problem = cp.Problem(objective, constraints)
m = mlopt.Optimizer(problem)
self.assertFalse(m._problem.parameters_in_matrices)