class TestSampling(unittest.TestCase):
def setUp(self):
# Generate data
np.random.seed(1)
T = 5
M = 2.
h = 1.
c = 1.
p = 1.
x_init = 2.
# Number of test points
n_test = 10
# Define problem
x = cp.Variable(T + 1)
u = cp.Variable(T)
# Define parameter and sampling points
d = cp.Parameter(T, nonneg=True, name="d")
# Constaints
constraints = [x[0] == x_init]
for t in range(T):
constraints += [x[t + 1] == x[t] + u[t] - d[t]]
constraints += [u >= 0, u <= M]
self.constraints = constraints
# Objective
self.cost = cp.sum(cp.maximum(h * x, -p * x)) + c * cp.sum(u)
# Define problem
problem = cp.Problem(cp.Minimize(self.cost), self.constraints)
self.optimizer = Optimizer(problem)
# Test set
self.df_test = sampling_function(n_test)
def test_sample(self):
"""Test sampling scheme"""
# Train optimizer
self.optimizer.train(sampling_fn=sampling_function,
learner=PYTORCH,
n_train_trials=10)
# Check tolerance
self.assertTrue(
self.optimizer._sampler.good_turing_smooth < s.SAMPLING_TOL)
# Check that smoothed is larger than unsmoothed
self.assertTrue(self.optimizer._sampler.good_turing <
self.optimizer._sampler.good_turing_smooth)
def test_save_load_data(self):
"""Test save load data"""
m = self.optimizer
for learner in installed_learners():
with tempfile.TemporaryDirectory() as tmpdir:
data_file = os.path.join(tmpdir, "data.pkl")
# Sample and store
m.train(
self.df,
# sampling_fn=lambda n: sample(self.d_bar,
# self.radius,
# n),
filter_strategies=True,
parallel=True,
learner=learner,
n_train_trials=10,
# params=nn_params
)
store_general, store_detail = m.performance(self.df_test)
# Save datafile
m.save_training_data(data_file, delete_existing=True)
# Create new optimizer, load data, train and
# evaluate performance
problem = cp.Problem(cp.Minimize(self.cost), self.constraints)
self.optimizer = Optimizer(problem)
m = self.optimizer
m.load_training_data(data_file)
m.train(parallel=True, learner=learner, n_train_trials=10)
load_general, load_detail = m.performance(self.df_test)
# test same things
npt.assert_almost_equal(store_general['max_infeas'],
load_general['max_infeas'],
decimal=1e-8)
npt.assert_almost_equal(store_general['avg_infeas'],
load_general['avg_infeas'],
decimal=1e-8)
npt.assert_almost_equal(store_general['max_subopt'],
load_general['max_subopt'],
decimal=1e-8)
npt.assert_almost_equal(store_general['avg_subopt'],
load_general['avg_subopt'],
decimal=1e-8)
def setUp(self):
# Generate data
np.random.seed(0)
T = 5
M = 2.
h = 1.
c = 1.
p = 1.
x_init = 2.
self.radius = 2.
n = 1000 # Number of points
n_test = 100
# Define problem
x = cp.Variable(T + 1)
u = cp.Variable(T)
# Define parameter and sampling points
d = cp.Parameter(T, nonneg=True, name="d")
self.d_bar = 2. * np.ones(T)
X_d = uniform_sphere_sample(self.d_bar, self.radius, n=n)
X_d_test = uniform_sphere_sample(self.d_bar, self.radius, n=n_test)
self.df = pd.DataFrame({'d': list(X_d)})
self.df_test = pd.DataFrame({'d': list(X_d_test)})
# Constaints
constraints = [x[0] == x_init]
for t in range(T):
constraints += [x[t + 1] == x[t] + u[t] - d[t]]
constraints += [u >= 0, u <= M]
self.constraints = constraints
# Objective
self.cost = cp.sum(cp.maximum(
h * x, -p * x)) + c * cp.sum_squares(u) + 0.001 * cp.sum_squares(x)
# Define problem
problem = cp.Problem(cp.Minimize(self.cost), self.constraints)
self.optimizer = Optimizer(problem)
def setUp(self):
# Generate data
np.random.seed(1)
T = 5
M = 2.
h = 1.
c = 1.
p = 1.
x_init = 2.
# Number of test points
n_test = 10
# Define problem
x = cp.Variable(T + 1)
u = cp.Variable(T)
# Define parameter and sampling points
d = cp.Parameter(T, nonneg=True, name="d")
# Constaints
constraints = [x[0] == x_init]
for t in range(T):
constraints += [x[t + 1] == x[t] + u[t] - d[t]]
constraints += [u >= 0, u <= M]
self.constraints = constraints
# Objective
self.cost = cp.sum(cp.maximum(h * x, -p * x)) + c * cp.sum(u)
# Define problem
problem = cp.Problem(cp.Minimize(self.cost), self.constraints)
self.optimizer = Optimizer(problem)
# Test set
self.df_test = sampling_function(n_test)
def test_save_load(self):
"""Test save load"""
for learner in installed_learners():
with tempfile.TemporaryDirectory() as tmpdir:
data_file = os.path.join(tmpdir, "data.pkl")
# Train optimizer
self.optimizer.train(self.df,
n_train_trials=10,
learner=learner)
# Create temporary directory where
# to do stuff
with tempfile.TemporaryDirectory() as tmpdir:
# Archive name
file_name = os.path.join(tmpdir, learner + ".tar.gz")
# Save optimizer
self.optimizer.save(file_name)
# Create new optimizer and load
new_optimizer = Optimizer.from_file(file_name)
# Predict with optimizer
res = self.optimizer.solve(self.df_test)
# Predict with new_optimizer
res_new = new_optimizer.solve(self.df_test)
# Make sure predictions match
for i in range(len(self.df_test)):
npt.assert_almost_equal(res[i]['x'],
res_new[i]['x'],
decimal=TOL)
npt.assert_almost_equal(res[i]['cost'],
res_new[i]['cost'],
decimal=TOL)
self.assertTrue(
res[i]['strategy'] == res_new[i]['strategy'])
class TestSave(unittest.TestCase):
def setUp(self):
# Generate data
np.random.seed(0)
T = 5
M = 2.
h = 1.
c = 1.
p = 1.
x_init = 2.
self.radius = 2.
n = 1000 # Number of points
n_test = 100
# Define problem
x = cp.Variable(T + 1)
u = cp.Variable(T)
# Define parameter and sampling points
d = cp.Parameter(T, nonneg=True, name="d")
self.d_bar = 2. * np.ones(T)
X_d = uniform_sphere_sample(self.d_bar, self.radius, n=n)
X_d_test = uniform_sphere_sample(self.d_bar, self.radius, n=n_test)
self.df = pd.DataFrame({'d': list(X_d)})
self.df_test = pd.DataFrame({'d': list(X_d_test)})
# Constaints
constraints = [x[0] == x_init]
for t in range(T):
constraints += [x[t + 1] == x[t] + u[t] - d[t]]
constraints += [u >= 0, u <= M]
self.constraints = constraints
# Objective
self.cost = cp.sum(cp.maximum(
h * x, -p * x)) + c * cp.sum_squares(u) + 0.001 * cp.sum_squares(x)
# Define problem
problem = cp.Problem(cp.Minimize(self.cost), self.constraints)
self.optimizer = Optimizer(problem)
# # Define learners
# self.learners = [
# # s.OPTIMAL_TREE, # Disable. Too slow
# s.PYTORCH
# ]
def test_save_load_data(self):
"""Test save load data"""
m = self.optimizer
for learner in installed_learners():
with tempfile.TemporaryDirectory() as tmpdir:
data_file = os.path.join(tmpdir, "data.pkl")
# Sample and store
m.train(
self.df,
# sampling_fn=lambda n: sample(self.d_bar,
# self.radius,
# n),
filter_strategies=True,
parallel=True,
learner=learner,
n_train_trials=10,
# params=nn_params
)
store_general, store_detail = m.performance(self.df_test)
# Save datafile
m.save_training_data(data_file, delete_existing=True)
# Create new optimizer, load data, train and
# evaluate performance
problem = cp.Problem(cp.Minimize(self.cost), self.constraints)
self.optimizer = Optimizer(problem)
m = self.optimizer
m.load_training_data(data_file)
m.train(parallel=True, learner=learner, n_train_trials=10)
load_general, load_detail = m.performance(self.df_test)
# test same things
npt.assert_almost_equal(store_general['max_infeas'],
load_general['max_infeas'],
decimal=1e-8)
npt.assert_almost_equal(store_general['avg_infeas'],
load_general['avg_infeas'],
decimal=1e-8)
npt.assert_almost_equal(store_general['max_subopt'],
load_general['max_subopt'],
decimal=1e-8)
npt.assert_almost_equal(store_general['avg_subopt'],
load_general['avg_subopt'],
decimal=1e-8)
#
def test_save_load(self):
"""Test save load"""
for learner in installed_learners():
with tempfile.TemporaryDirectory() as tmpdir:
data_file = os.path.join(tmpdir, "data.pkl")
# Train optimizer
self.optimizer.train(self.df,
n_train_trials=10,
learner=learner)
# Create temporary directory where
# to do stuff
with tempfile.TemporaryDirectory() as tmpdir:
# Archive name
file_name = os.path.join(tmpdir, learner + ".tar.gz")
# Save optimizer
self.optimizer.save(file_name)
# Create new optimizer and load
new_optimizer = Optimizer.from_file(file_name)
# Predict with optimizer
res = self.optimizer.solve(self.df_test)
# Predict with new_optimizer
res_new = new_optimizer.solve(self.df_test)
# Make sure predictions match
for i in range(len(self.df_test)):
npt.assert_almost_equal(res[i]['x'],
res_new[i]['x'],
decimal=TOL)
npt.assert_almost_equal(res[i]['cost'],
res_new[i]['cost'],
decimal=TOL)
self.assertTrue(
res[i]['strategy'] == res_new[i]['strategy'])