# %% [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,
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,
fig=fig,
)
fig.show()
# %% [markdown]
# We can also visualise the how each successive point compares the current best.
#
# We produce two plots. The left hand plot shows the observations (crosses and dots), the current best (orange line), and the start of the optimization loop (blue line). The right hand plot is the same as the previous two-dimensional contour plot, but without the resulting observations. The best point is shown in each (purple dot).
# %%
import matplotlib.pyplot as plt
from util.plotting import plot_regret
suboptimality = observations - SCALED_BRANIN_MINIMUM.numpy()
# %%
from util.plotting_plotly import plot_gp_plotly, add_bo_points_plotly
arg_min_idx = tf.squeeze(
tf.argmin(result.datasets[OBJECTIVE].observations, axis=0))
fig = plot_gp_plotly(result.models[OBJECTIVE].model,
search_space.lower,
search_space.upper,
grid_density=50)
fig = add_bo_points_plotly(
x=result.datasets[OBJECTIVE].query_points[:, 0].numpy(),
y=result.datasets[OBJECTIVE].query_points[:, 1].numpy(),
z=result.datasets[OBJECTIVE].observations.numpy().flatten(),
num_init=num_init_points,
idx_best=arg_min_idx,
fig=fig,
figrow=1,
figcol=1,
)
fig.show()
# %% [markdown]
# ... and then for the failure data and model
# %%
fig, ax = plot_gp_2d(
result.models[FAILURE].model,
search_space.lower,
search_space.upper,