def plot_active_learning_query(result, bo_iter, num_initial_points, query_points, num_query=1):
for i in range(bo_iter):
def pred_var(x):
_, var = result.history[i].models["OBJECTIVE"].model.predict_f(x)
return var
_, ax = plot_function_2d(
pred_var,
search_space.lower - 0.01,
search_space.upper + 0.01,
grid_density=100,
contour=True,
colorbar=True,
figsize=(10, 6),
title=["Variance contour with queried points at iter:" + str(i + 1)],
xlabel="$X_1$",
ylabel="$X_2$",
)
plot_bo_points(
query_points[: num_initial_points + (i * num_query)], ax[0, 0], num_initial_points
)
observations = dataset.observations.numpy()
arg_min_idx = tf.squeeze(tf.argmin(observations, axis=0))
print(f"query point: {query_points[arg_min_idx, :]}")
print(f"observation: {observations[arg_min_idx, :]}")
# %% [markdown]
# We can visualise how the optimizer performed by plotting all the acquired observations, along with the true function values and optima, either in a two-dimensional contour plot ...
# %%
from util.plotting import plot_bo_points, plot_function_2d
_, ax = plot_function_2d(scaled_branin,
search_space.lower,
search_space.upper,
grid_density=30,
contour=True)
plot_bo_points(query_points, ax[0, 0], num_initial_points, arg_min_idx)
ax[0, 0].set_xlabel(r'$x_1$')
ax[0, 0].set_xlabel(r'$x_2$')
# %% [markdown]
# ... or as a three-dimensional plot
# %%
from util.plotting_plotly import add_bo_points_plotly
fig = plot_function_plotly(scaled_branin,
search_space.lower,
search_space.upper,
observer.manual_fix()
result, new_history = bo.optimize(
15 - len(history),
history[-1].datasets,
history[-1].models,
acquisition_rule,
history[-1].acquisition_state
).astuple()
history.extend(new_history)
# %% [markdown]
# We can repeat this until we've spent our optimization budget, using a loop if appropriate. But here, we'll just plot the data if it exists, safely by using `result`'s `is_ok` attribute.
# %%
from util.plotting import plot_bo_points, plot_function_2d
if result.is_ok:
data = result.unwrap().datasets[OBJECTIVE]
arg_min_idx = tf.squeeze(tf.argmin(data.observations, axis=0))
_, ax = plot_function_2d(
branin, search_space.lower, search_space.upper, 30, contour=True
)
plot_bo_points(data.query_points.numpy(), ax[0, 0], 5, arg_min_idx)
# %% [markdown]
# ## LICENSE
#
# [Apache License 2.0](https://github.com/secondmind-labs/trieste/blob/develop/LICENSE)
tf.argmin(result.datasets[OBJECTIVE].observations, axis=0))
print(
f"query point: {result.datasets[OBJECTIVE].query_points[arg_min_idx, :]}")
# %% [markdown]
# We can visualise where the optimizer queried on a contour plot of the Branin with the failure region. The minimum observation can be seen along the bottom axis towards the right, outside of the failure region.
# %%
import matplotlib.pyplot as plt
from util.plotting import plot_gp_2d, plot_function_2d, plot_bo_points
mask_fail = result.datasets[FAILURE].observations.numpy().flatten().astype(
int) == 0
fig, ax = plot_function_2d(masked_branin,
search_space.lower,
search_space.upper,
grid_density=50,
contour=True)
plot_bo_points(
result.datasets[FAILURE].query_points.numpy(),
ax=ax[0, 0],
num_init=num_init_points,
mask_fail=mask_fail,
)
plt.show()
# %% [markdown]
# We can also plot the mean and variance of the predictive distribution over the search space, first for the objective data and model ...
# %%
from util.plotting_plotly import plot_gp_plotly, add_bo_points_plotly
result = bo.optimize(20, initial_data, models, acquisition_rule=rule)
if result.error is not None: raise result.error
final_data = result.datasets
arg_min_idx = tf.squeeze(tf.argmin(final_data[OBJECTIVE].observations, axis=0))
print(f"query point: {final_data[OBJECTIVE].query_points[arg_min_idx, :]}")
# %% [markdown]
# We can visualise where the optimizer queried on a contour plot of the Branin with the failure region. The minimum observation can be seen along the bottom axis towards the right, outside of the failure region.
# %%
mask_fail = final_data[FAILURE].observations.numpy().flatten().astype(int) == 0
fig, ax = plot_function_2d(masked_branin, mins, maxs, grid_density=50, contour=True)
plot_bo_points(
final_data[FAILURE].query_points.numpy(),
ax=ax[0, 0],
num_init=num_init_points,
mask_fail=mask_fail,
)
plt.show()
# %% [markdown]
# We can also plot the mean and variance of the predictive distribution over the search space, first for the objective data and model ...
# %%
arg_min_idx = tf.squeeze(tf.argmin(final_data[OBJECTIVE].observations, axis=0))
fig = plot_gp_plotly(regression_model, mins, maxs, grid_density=50)
if result.error is not None: raise result.error
dataset = result.datasets[OBJECTIVE]
# %% [markdown]
# ## Visualising the result
#
# We can take a look at where we queried the observer, both the original query points (crosses) and new query points (dots), and where they lie with respect to the contours of the Branin.
# %%
arg_min_idx = tf.squeeze(tf.argmin(dataset.observations, axis=0))
query_points = dataset.query_points.numpy()
observations = dataset.observations.numpy()
_, ax = plot_function_2d(
branin, lower_bound.numpy(), upper_bound.numpy(), grid_density=30, contour=True
)
plot_bo_points(query_points, ax[0, 0], num_initial_data_points, arg_min_idx)
# %% [markdown]
# We can also visualise the observations on a three-dimensional plot of the Branin. We'll add the contours of the mean and variance of the model's predictive distribution as translucent surfaces.
# %%
fig = plot_gp_plotly(gpr, lower_bound.numpy(), upper_bound.numpy(), grid_density=30)
fig = add_bo_points_plotly(
x=query_points[:, 0],
y=query_points[:, 1],
z=observations[:, 0],
num_init=num_initial_data_points,
idx_best=arg_min_idx,
# %%
num_initial_points = 20
initial_query_points = search_space.sample(num_initial_points)
initial_data = observer(initial_query_points)
# %% [markdown]
# ... and visualise the data across the design space: each figure contains the contour lines of each objective function.
# %%
_, ax = plot_function_2d(
vlmop2,
mins,
maxs,
grid_density=100,
contour=True,
title=["Obj 1", "Obj 2"],
figsize=(12, 6),
colorbar=True,
xlabel="$X_1$",
ylabel="$X_2$",
)
plot_bo_points(initial_query_points, ax=ax[0, 0], num_init=num_initial_points)
plot_bo_points(initial_query_points, ax=ax[0, 1], num_init=num_initial_points)
plt.show()
# %% [markdown]
# ... and in the objective space. The `plot_mobo_points_in_obj_space` will automatically search for non-dominated points and colours them in purple.
# %%
plot_mobo_points_in_obj_space(initial_data.observations)
plt.show()
# We can now get the best point found by the optimizer. Note this isn't necessarily the point that was last evaluated.
# %%
query_points = dataset.query_points.numpy()
observations = dataset.observations.numpy()
arg_min_idx = tf.squeeze(tf.argmin(observations, axis=0))
print(f"query point: {query_points[arg_min_idx, :]}")
print(f"observation: {observations[arg_min_idx, :]}")
# %% [markdown]
# We can visualise how the optimizer performed by plotting all the acquired observations, along with the true function values and optima, either in a two-dimensional contour plot ...
# %%
_, ax = plot_function_2d(branin, mins, maxs, grid_density=30, contour=True)
plot_bo_points(query_points, ax[0, 0], num_initial_points, arg_min_idx)
# %% [markdown]
# ... or as a three-dimensional plot
# %%
fig = plot_function_plotly(branin, mins, maxs, grid_density=20)
fig.update_layout(height=500, width=500)
fig = add_bo_points_plotly(
x=query_points[:, 0],
y=query_points[:, 1],
z=observations[:, 0],
num_init=num_initial_points,
idx_best=arg_min_idx,
