示例#1
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

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]
示例#2
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               idx_best=arg_min_idx)

ax[0].set_yscale("log")
ax[0].set_ylabel("Regret")
ax[0].set_ylim(0.001, 100)
ax[0].set_xlabel("# evaluations")

# %% [markdown]
# We can visualise the model over the objective function by plotting the mean and 95% confidence intervals of its predictive distribution. Like with the data before, we can get the model with `.try_get_final_model()`.

# %%
from util.plotting_plotly import plot_gp_plotly

fig = plot_gp_plotly(
    result.try_get_final_model().model,  # type: ignore
    search_space.lower,
    search_space.upper,
    grid_density=30,
)

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,
    figrow=1,
    figcol=1,
)
fig.show()
示例#3
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_, ax = plt.subplots(1, 2)
plot_regret(observations, ax[0], num_init=num_initial_points, idx_best=arg_min_idx)
plot_bo_points(
    query_points, ax[1], num_init=num_initial_points, idx_best=arg_min_idx
)

# %% [markdown]
# We can visualise the model over the objective function by plotting the mean and 95% confidence intervals of its predictive distribution. Like with the data before, we can get the model with `.try_get_final_models()` and indexing with `OBJECTIVE`.

# %%
from util.plotting_plotly import plot_gp_plotly

fig = plot_gp_plotly(
    result.try_get_final_models()[OBJECTIVE].model,
    search_space.lower,
    search_space.upper,
    grid_density=30
)

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,
    figrow=1,
    figcol=1,
)

fig.show()
示例#4
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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)
fig = add_bo_points_plotly(
    x=final_data[OBJECTIVE].query_points[:, 0].numpy(),
    y=final_data[OBJECTIVE].query_points[:, 1].numpy(),
    z=final_data[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
示例#5
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# ## 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,
    fig=fig,
    figrow=1,
    figcol=1,
)
fig.show()
示例#6
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_, ax = plt.subplots(1, 2)
plot_regret(observations,
            ax[0],
            num_init=num_initial_points,
            idx_best=arg_min_idx)
plot_bo_points(query_points,
               ax[1],
               num_init=num_initial_points,
               idx_best=arg_min_idx)

# %% [markdown]
# We can visualise the model over the objective function by plotting the mean and 95% confidence intervals of its predictive distribution.

# %%
fig = plot_gp_plotly(model[OBJECTIVE].model, mins, maxs, grid_density=30)

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,
    figrow=1,
    figcol=1,
)

fig.show()

# %% [markdown]
示例#7
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_, ax = plt.subplots(1, 2)
plot_regret(observations,
            ax[0],
            num_init=num_initial_points,
            idx_best=arg_min_idx)
plot_bo_points(query_points,
               ax[1],
               num_init=num_initial_points,
               idx_best=arg_min_idx)

# %% [markdown]
# We can visualise the model over the objective function by plotting the mean and 95% confidence
# intervals of its predictive distribution.

# %%
fig = plot_gp_plotly(gpr, mins, maxs, grid_density=30)

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,
    figrow=1,
    figcol=1,
)

fig.show()

# %% [markdown]