def test_simple_hpo():
def f(args):
x = args['x']
return x*x
s = {'x': {'dist': st.uniform(loc=-10., scale=20), 'lo': -10., 'hi': 10.}}
trials = []
#Test fmin and ability to continue adding to trials
best = fmin(loss_fn=f, space=s, max_evals=40, trials=trials)
best = fmin(loss_fn=f, space=s, max_evals=10, trials=trials)
assert len(trials) == 50, "HPO continuation trials not working"
# Test verbose flag
best = fmin(loss_fn=f, space=s, max_evals=10, trials=trials)
yarray = np.array([tr['loss'] for tr in trials])
np.testing.assert_array_less(yarray, 100.)
xarray = np.array([tr['x'] for tr in trials])
np.testing.assert_array_less(np.abs(xarray), 10.)
assert best['loss'] < 100., "HPO out of range"
assert np.abs(best['x']) < 10., "HPO out of range"
#Test unknown distributions
s2 = {'x': {'dist': 'normal', 'mu': 0., 'sigma': 1.}}
trials2 = []
with pytest.raises(ValueError) as excinfo:
best2 = fmin(loss_fn=f, space=s2, max_evals=40, trials=trials2)
assert "Unknown distribution type for variable" in str(excinfo.value)
s3 = {'x': {'dist': st.norm(loc=0., scale=1.)}}
trials3 = []
best3 = fmin(loss_fn=f, space=s3, max_evals=40, trials=trials3)
def test_simple_hpo():
def f(args):
x = args['x']
return x * x
s = {'x': {'dist': st.uniform(loc=-10., scale=20), 'lo': -10., 'hi': 10.}}
trials = []
#Test fmin and ability to continue adding to trials
best = fmin(loss_fn=f, space=s, max_evals=40, trials=trials)
best = fmin(loss_fn=f, space=s, max_evals=10, trials=trials)
assert len(trials) == 50, "HPO continuation trials not working"
# Test verbose flag
best = fmin(loss_fn=f, space=s, max_evals=10, trials=trials)
yarray = np.array([tr['loss'] for tr in trials])
np.testing.assert_array_less(yarray, 100.)
xarray = np.array([tr['x'] for tr in trials])
np.testing.assert_array_less(np.abs(xarray), 10.)
assert best['loss'] < 100., "HPO out of range"
assert np.abs(best['x']) < 10., "HPO out of range"
#Test unknown distributions
s2 = {'x': {'dist': 'normal', 'mu': 0., 'sigma': 1.}}
trials2 = []
with pytest.raises(ValueError) as excinfo:
best2 = fmin(loss_fn=f, space=s2, max_evals=40, trials=trials2)
assert "Unknown distribution type for variable" in str(excinfo.value)
s3 = {'x': {'dist': st.norm(loc=0., scale=1.)}}
trials3 = []
best3 = fmin(loss_fn=f, space=s3, max_evals=40, trials=trials3)
# The trials object is just a list that stores the samples generated and the
# corresponding function values at those sample points.
trials = []
# Maximum number of samples that will be generated.
# This is the maximum number of function evaluations that will be performed.
n_hpo_samples = 100
# Call the fmin function that does the optimization.
# The function to be optimized should take in a dictionary. You will probably
# need to wrap your function to do this (see branin() and branin_wrapper()).
# You can pass in a non-empty trials object as well e.g. from a previous
# fmin run. We just append to the trials object and will use existing data
# in our optimization.
print("Starting optimization through hpo")
best = hpo.fmin(loss_fn=branin_wrapper, space=space,
max_evals=n_hpo_samples, trials=trials)
# Print out the best value obtained through HPO
print("Best obtained through HPO (", n_hpo_samples, " samples) = ",
best['x1'], best['x2'], "; min value = ", best['loss'])
#####################################
# Optimization through grid search
#####################################
# Divide the space into a uniform grid (meshgrid)
n = 200
x1 = np.linspace(x1lo, x1hi, n)
x2 = np.linspace(x2lo, x2hi, n)
x1_grid, x2_grid = np.meshgrid(x1, x2)
# corresponding function values at those sample points.
trials = []
# Maximum number of samples that will be generated.
# This is the maximum number of function evaluations that will be performed.
n_hpo_samples = 100
# Call the fmin function that does the optimization.
# The function to be optimized should take in a dictionary. You will probably
# need to wrap your function to do this (see branin() and branin_wrapper()).
# You can pass in a non-empty trials object as well e.g. from a previous
# fmin run. We just append to the trials object and will use existing data
# in our optimization.
print("Starting optimization through hpo")
best = hpo.fmin(loss_fn=branin_wrapper,
space=space,
max_evals=n_hpo_samples,
trials=trials)
# Print out the best value obtained through HPO
print("Best obtained through HPO (", n_hpo_samples, " samples) = ", best['x1'],
best['x2'], "; min value = ", best['loss'])
#####################################
# Optimization through grid search
#####################################
# Divide the space into a uniform grid (meshgrid)
n = 200
x1 = np.linspace(x1lo, x1hi, n)
x2 = np.linspace(x2lo, x2hi, n)
x1_grid, x2_grid = np.meshgrid(x1, x2)
