def draw_final_figure(sample_cost, vis_confs, inc_eq_loc_x, draw_improvement=True, draw_normals=True):
fig, ax = draw_basic_figure_plus_zone()
labels = [r'$\lambda_%d$' % (idx + 1) for idx in range(len(vis_confs))]
boplot.highlight_configuration(
x=np.array(vis_confs),
label=labels,
lloc='bottom',
ax=ax,
disable_ticks=True
)
for label, conf in zip(labels, vis_confs):
boplot.annotate_x_edge(
label=label,
xy=(conf + 0.6 * (ax.get_xlim()[1] - ax.get_xlim()[0]) / 10, ymin),
ax=ax,
align='bottom',
offset_param=1.9
)
boplot.highlight_output(
y=np.array([sample_cost, ymin]),
label=['c', '$c_{inc}$'],
lloc='left',
ax=ax
)
# boplot.annotate_y_edge(label=r'c', xy=(lambda, cost), align='left', ax=ax)
if draw_improvement:
ax.annotate(s='', xy=(inc_eq_loc_x, sample_cost), xytext=(inc_eq_loc_x, ymin),
arrowprops={'arrowstyle': 'simple', })
ax.text(inc_eq_loc_x - 1.0, sample_cost - 1.0, r'$I_c=c_{inc}-c$', weight='heavy')
if draw_normals:
for idx in range(len(vis_confs)):
conf = vis_confs[idx]
draw_distribution_for_candidate(ax=ax, candidate=conf, target_cost=sample_cost)
ax.annotate(
s=r"$p(c|\lambda_%d)$" % (idx+1), xy=(conf, sample_cost), xytext=(conf-1.8, sample_cost - 1.5),
arrowprops={'arrowstyle': 'fancy', 'shrinkA': 20.0},
weight='heavy', color='darkgreen', zorder=zorders['annotations_high']
)
return fig, ax
def visualize_look_ahead(initial_design, init=None):
"""
Visualize one-step of look-ahead.
:param initial_design: Method for initializing the GP, choice between 'uniform', 'random', and 'presentation'
:param init: Number of datapoints to initialize GP with.
:return: None
"""
# boplot.set_rcparams(**{'legend.loc': 'lower left'})
logging.debug(
"Visualizing Look-Ahead with initial design {} and init {}".format(
initial_design, init))
# Initialize dummy dataset
x, y = initialize_dataset(initial_design=initial_design, init=init)
logging.debug(
"Initialized dataset with:\nsamples {0}\nObservations {1}".format(
x, y))
# Fit GP to the currently available dataset
gp = GPR(kernel=Matern())
logging.debug("Fitting GP to\nx: {}\ny:{}".format(x, y))
gp.fit(x, y) # fit the model
mu_star_t_xy = get_mu_star(gp)
logging.info("Mu-star at time t: {}".format(mu_star_t_xy))
# noinspection PyStringFormat
logging.debug(
"Model fit to dataset.\nOriginal Inputs: {0}\nOriginal Observations: {1}\n"
"Predicted Means: {2}\nPredicted STDs: {3}".format(
x, y, *(gp.predict(x, return_std=True))))
# Assume next evaluation location
x_ = np.array([[5.0]])
print(x_)
y_ = f(x_[0])
# Update dataset with new observation
X2_ = np.append(x, x_, axis=0)
Y2_ = y + [y_]
logging.info("x: {}, y: {}".format(x_, y_))
# Fit GP to the updated dataset
gp2 = GPR(kernel=Matern())
logging.debug("Fitting GP to\nx: {}\ny:{}".format(X2_, Y2_))
gp2.fit(X2_, Y2_) # fit the model
mu_star_t1_xy = get_mu_star(gp2)
logging.info("Mu-star at time t+1: {}".format(mu_star_t1_xy))
# -------------------------Plotting madness begins---------------------------
def draw_basic_figure(tgp=gp,
tx=x,
tX_=x,
tY_=y,
title='',
highlight_datapoint=None,
highlight_label="",
ax=None):
if ax is None:
fig, ax = plt.subplots(1, 1, squeeze=True)
plt.subplots_adjust(0.05, 0.15, 0.95, 0.85)
figflag = True
else:
figflag = False
ax.set_xlim(bounds["x"])
ax.set_ylim(bounds["gp_y"])
if title:
ax.set_title(title, loc='left')
ax.grid()
boplot.plot_objective_function(ax=ax)
boplot.plot_gp(model=tgp,
confidence_intervals=[1.0, 2.0, 3.0],
ax=ax,
custom_x=tx)
if highlight_datapoint:
boplot.mark_observations(X_=tX_,
Y_=tY_,
mark_incumbent=False,
ax=ax,
highlight_datapoint=highlight_datapoint,
highlight_label=highlight_label)
else:
boplot.mark_observations(X_=tX_,
Y_=tY_,
mark_incumbent=False,
ax=ax)
if figflag:
return fig, ax
else:
return ax
def perform_finishing_tasks(ax, filename="", remove_legend=True):
ax.legend().set_zorder(zorders['legend'])
ax.set_xlabel(labels['xlabel'])
if remove_legend:
ax.legend().remove()
# plt.tight_layout()
if TOGGLE_PRINT:
# plt.savefig(f"{OUTPUT_DIR}/{filename}", bbox_inches='tight')
plt.savefig(f"{OUTPUT_DIR}/{filename}")
else:
plt.show()
# ---------------------------------------
# Draw look ahead 1.
labels['gp_mean'] = r'Mean: $\mu^{(t)}(\cdot)$'
fig, ax = draw_basic_figure(title="")
perform_finishing_tasks(ax=ax,
filename="look_ahead_1.pdf",
remove_legend=False)
# ---------------------------------------
# Draw look ahead 2
fig, ax = draw_basic_figure(title="")
logging.debug("Placing vertical on configuration: {}".format(x_))
# boplot.highlight_configuration(x=x_, label='', lloc='bottom', ax=ax, ha='center')
boplot.highlight_configuration(x=x_,
label='',
lloc='bottom',
ax=ax,
disable_ticks=True)
boplot.annotate_x_edge(label=r'$\lambda$',
xy=(x_, y_),
align='bottom',
ax=ax)
perform_finishing_tasks(ax=ax,
filename="look_ahead_2.pdf",
remove_legend=True)
# ---------------------------------------
# Draw look ahead 3
fig, ax = draw_basic_figure(title="")
boplot.highlight_configuration(x=x_,
label='',
lloc='bottom',
ax=ax,
ha='right')
boplot.annotate_x_edge(label=r'$\lambda$',
xy=(x_, y_),
align='bottom',
ax=ax)
boplot.highlight_output(y_, label='', lloc='right', ax=ax, fontsize=28)
boplot.annotate_y_edge(label=r'$c(\lambda)$',
xy=(x_, y_),
align='right',
ax=ax)
ax.scatter(x_,
y_,
color=colors['highlighted_observations'],
marker='X',
label=r"Hypothetical Observation $<\lambda, c(\lambda)>$",
zorder=zorders['annotations_normal'])
perform_finishing_tasks(ax=ax,
filename="look_ahead_3.pdf",
remove_legend=True)
# ---------------------------------------
# Draw look ahead 4.
labels['gp_mean'] = r'Mean: $\mu^{(t+1)}(\cdot)|_\lambda$'
fig, ax = draw_basic_figure(
tgp=gp2,
tx=x,
tX_=X2_,
tY_=Y2_,
title='',
highlight_datapoint=np.where(np.isclose(X2_, x_))[0],
highlight_label=r"Hypothetical Observation $<\lambda, c(\lambda)>$")
perform_finishing_tasks(ax=ax,
filename="look_ahead_4.pdf",
remove_legend=False)
# ---------------------------------------
# Vertical comparison of look-ahead at any given x
def draw_vertical_comparison(imaginary_lambda, ax1, ax2):
tx_ = np.array([[imaginary_lambda]])
ty_ = f(tx_[0])
# Update dataset with new observation
tX_ = np.append(x, tx_, axis=0)
tY_ = y + [ty_]
logging.info("x: {}, y: {}".format(tx_, ty_))
# Fit GP to the updated dataset
tgp = GPR(kernel=Matern())
logging.debug("Fitting GP to\nx: {}\ny:{}".format(tX_, tY_))
tgp.fit(tX_, tY_) # fit the model
tmu_star_t1_xy = get_mu_star(tgp)
# Draw the left hand figure using the old gp on ax1
draw_basic_figure(tgp=gp, title=r"$\hat{c}^{(t)}$", ax=ax1)
logging.debug("Placing vertical on configuration: {}".format(tx_))
ax1.scatter(tx_,
ty_,
color=colors['highlighted_observations'],
marker='X',
label=r"Hypothetical Observation $<\lambda, c(\lambda)>$",
zorder=zorders["annotations_normal"])
ax1.legend().remove()
# Draw the right hand figure using the hypothetical gp tgp on ax2
draw_basic_figure(
tgp=tgp,
tx=tX_,
tX_=tX_,
tY_=tY_,
title=r"$\hat{c}^{(t+1)}|_\lambda$",
highlight_datapoint=np.where(np.isclose(tX_, tx_))[0],
highlight_label=r"Hypothetical Observation $<\lambda, c(\lambda)>$",
ax=ax2)
def finishing_touches_parallel(ax1, ax2, filename=""):
ax1.set_xlabel(labels['xlabel'])
ax2.set_xlabel(labels['xlabel'])
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig(f"{OUTPUT_DIR}/{filename}")
else:
plt.show()
# ---------------------------------------
# Draw look ahead 5
fig, (ax1, ax2) = plt.subplots(1, 2, squeeze=True, figsize=(22, 9))
draw_vertical_comparison(imaginary_lambda=5.0, ax1=ax1, ax2=ax2)
finishing_touches_parallel(ax1=ax1, ax2=ax2, filename="look_ahead_5.pdf")
# ---------------------------------------
# Draw look ahead 6
fig, (ax1, ax2) = plt.subplots(1, 2, squeeze=True, figsize=(22, 9))
draw_vertical_comparison(imaginary_lambda=5.5, ax1=ax1, ax2=ax2)
finishing_touches_parallel(ax1=ax1, ax2=ax2, filename="look_ahead_6.pdf")
# ---------------------------------------
# Draw look ahead 5
fig, (ax1, ax2) = plt.subplots(1, 2, squeeze=True, figsize=(22, 9))
draw_vertical_comparison(imaginary_lambda=3.5, ax1=ax1, ax2=ax2)
finishing_touches_parallel(ax1=ax1, ax2=ax2, filename="look_ahead_7.pdf")
# ---------------------------------------
# Draw KG 1
labels['gp_mean'] = r'Mean: $\mu^{(t)}(\cdot)$'
fig, ax = draw_basic_figure(title="")
perform_finishing_tasks(ax=ax, filename="kg_1.pdf", remove_legend=False)
# ---------------------------------------
# Draw kg 2
fig, ax = draw_basic_figure(title="")
boplot.highlight_configuration(mu_star_t_xy[0],
lloc='bottom',
ax=ax,
disable_ticks=True)
boplot.annotate_x_edge(label="%.2f" % mu_star_t_xy[0],
xy=mu_star_t_xy,
ax=ax,
align='bottom',
offset_param=1.5)
boplot.highlight_output(mu_star_t_xy[1],
label='',
lloc='right',
ax=ax,
fontsize=30,
disable_ticks=True)
boplot.annotate_y_edge(label=r'${(\mu^*)}^{(t)}$',
xy=mu_star_t_xy,
align='right',
ax=ax,
yoffset=1.5)
perform_finishing_tasks(ax=ax, filename="kg_2.pdf", remove_legend=True)
# ---------------------------------------
# Draw kg 3
fig, ax = draw_basic_figure(
tgp=gp2,
tx=x,
tX_=X2_,
tY_=Y2_,
title='',
highlight_datapoint=np.where(np.isclose(X2_, x_))[0],
highlight_label=r"Hypothetical Observation $<\lambda, c(\lambda)>$")
perform_finishing_tasks(ax=ax, filename="kg_3.pdf", remove_legend=True)
# ---------------------------------------
# Draw kg 4
fig, ax = draw_basic_figure(
tgp=gp2,
tx=x,
tX_=X2_,
tY_=Y2_,
title='',
highlight_datapoint=np.where(np.isclose(X2_, x_))[0],
highlight_label=r"Hypothetical Observation $<\lambda, c(\lambda)>$")
boplot.highlight_configuration(mu_star_t1_xy[0],
lloc='bottom',
ax=ax,
disable_ticks=True)
boplot.annotate_x_edge(label="%.2f" % mu_star_t1_xy[0],
xy=mu_star_t1_xy,
ax=ax,
align='bottom',
offset_param=1.5)
boplot.highlight_output(mu_star_t1_xy[1],
label='',
lloc='right',
ax=ax,
fontsize=28)
boplot.annotate_y_edge(label=r'${(\mu^*)}^{(t+1)}|_\lambda$',
xy=mu_star_t1_xy,
align='right',
ax=ax,
yoffset=1.5)
perform_finishing_tasks(ax=ax, filename="kg_4.pdf", remove_legend=True)
# ---------------------------------------
# Draw kg 5
fig, (ax1, ax2) = plt.subplots(1, 2, squeeze=True, figsize=(22, 9))
draw_vertical_comparison(imaginary_lambda=x_.squeeze(), ax1=ax1, ax2=ax2)
boplot.highlight_output(mu_star_t_xy[1],
label='',
lloc='right',
ax=ax1,
fontsize=30)
boplot.annotate_y_edge(label='${(\mu^*)}^{(t)}$',
xy=mu_star_t_xy,
align='right',
ax=ax1,
yoffset=1.5)
boplot.highlight_output(mu_star_t1_xy[1],
label='',
lloc='right',
ax=ax2,
fontsize=28)
boplot.annotate_y_edge(label='${(\mu^*)}^{(t+1)}|_\lambda$',
xy=mu_star_t1_xy,
align='left',
ax=ax2,
yoffset=1.5)
finishing_touches_parallel(ax1=ax1, ax2=ax2, filename="kg_5.pdf")
return
def visualize_es(initial_design, init=None):
"""
Visualize one-step of ES.
:param initial_design: Method for initializing the GP, choice between 'uniform', 'random', and 'presentation'
:param init: Number of datapoints to initialize GP with.
:return: None
"""
# 1. Show GP fit on initial dataset, 0 samples, histogram
# 2. Show GP fit on initial dataset, 1 sample, histogram
# 3. Show GP fit on initial dataset, 3 samples, histogram
# 4. Show GP fit on initial dataset, 50 samples, histogram
# 5. Show PDF derived from the histogram at 50 samples
# 6. Mark maximum of the PDF as next configuration to be evaluated
# a. Plot GP
# b. Sample GP, mark minima, update histogram of lambda*
# c. Repeat 2 for each sample.
# d. Show results after multiple iterations
boplot.set_rcparams(**{'figure.figsize': (22, 11)})
# Initial setup
# -------------------------------------------
logging.debug("Visualizing ES with initial design {} and init {}".format(
initial_design, init))
# Initialize dummy dataset
x, y = initialize_dataset(initial_design=initial_design, init=init)
logging.debug(
"Initialized dataset with:\nsamples {0}\nObservations {1}".format(
x, y))
# Fit GP to the currently available dataset
gp = GPR(kernel=Matern())
logging.debug("Fitting GP to\nx: {}\ny:{}".format(x, y))
gp.fit(x, y) # fit the model
histogram_precision = 20
X_ = boplot.get_plot_domain(precision=histogram_precision)
nbins = X_.shape[0]
logging.info("Creating histograms with {} bins".format(nbins))
bin_range = (bounds['x'][0], bounds['x'][1] + 1 / histogram_precision)
# -------------------------------------------
def draw_samples(nsamples, ax1, ax2, show_min=False, return_pdf=False):
if not nsamples:
return
seed2 = 1256
seed3 = 65
mu = gp.sample_y(X=X_, n_samples=nsamples, random_state=seed3)
boplot.plot_gp_samples(mu=mu,
nsamples=nsamples,
precision=histogram_precision,
custom_x=X_,
show_min=show_min,
ax=ax1,
seed=seed2)
data_h = X_[np.argmin(mu, axis=0), 0]
logging.info("Shape of data_h is {}".format(data_h.shape))
logging.debug("data_h is: {}".format(data_h))
bins = ax2.hist(data_h,
bins=nbins,
range=bin_range,
density=return_pdf,
color='lightgreen',
edgecolor='black',
alpha=0.0 if return_pdf else 1.0)
return bins
# 1. Show GP fit on initial dataset, 0 samples, histogram
# -------------------------------------------
ax2_title = r'$p_{min}=P(\lambda=\lambda^*)$'
bounds['acq_y'] = (0.0, 1.0)
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True)
ax1.set_xlim(bounds['x'])
ax1.set_ylim(bounds['gp_y'])
ax2.set_xlim(bounds['x'])
ax2.set_ylim(bounds['acq_y'])
ax1.grid()
ax2.grid()
boplot.plot_objective_function(ax=ax1)
boplot.plot_gp(model=gp, confidence_intervals=[3.0], ax=ax1, custom_x=x)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=False, ax=ax1)
nsamples = 0
draw_samples(nsamples=nsamples, ax1=ax1, ax2=ax2, show_min=True)
# Plot uniform prior for p_min
xplot = boplot.get_plot_domain()
ylims = ax2.get_ylim()
xlims = ax2.get_xlim()
yupper = [(ylims[1] - ylims[0]) / (xlims[1] - xlims[0])] * xplot.shape[0]
ax2.plot(xplot[:, 0], yupper, color='green', linewidth=2.0)
ax2.fill_between(xplot[:, 0], ylims[0], yupper, color='lightgreen')
ax1.legend().set_zorder(20)
ax1.set_xlabel(labels['xlabel'])
ax1.set_ylabel(labels['gp_ylabel'])
ax1.set_title(r"Visualization of $\mathcal{G}^t$", loc='left')
ax2.set_xlabel(labels['xlabel'])
ax2.set_ylabel(r'$p_{min}$')
ax2.set_title(ax2_title, loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig('es_1')
else:
plt.show()
# -------------------------------------------
# 2. Show GP fit on initial dataset, 1 sample, histogram
# -------------------------------------------
bounds['acq_y'] = (0.0, 5.0)
ax2_title = r'Frequency of $\lambda=\hat{\lambda}^*$'
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True)
ax1.set_xlim(bounds['x'])
ax1.set_ylim(bounds['gp_y'])
ax2.set_xlim(bounds['x'])
ax2.set_ylim(bounds['acq_y'])
ax1.grid()
ax2.grid()
boplot.plot_objective_function(ax=ax1)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=False, ax=ax1)
nsamples = 1
draw_samples(nsamples=nsamples, ax1=ax1, ax2=ax2, show_min=True)
ax1.legend().set_zorder(20)
ax1.set_xlabel(labels['xlabel'])
ax1.set_ylabel(labels['gp_ylabel'])
ax1.set_title(r"One sample from $\mathcal{G}^t$", loc='left')
ax2.set_xlabel(labels['xlabel'])
# ax2.set_ylabel(r'$p_{min}$')
ax2.set_ylabel(r'Frequency')
ax2.set_title(ax2_title, loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig('es_2')
else:
plt.show()
# 3. Show GP fit on initial dataset, 10 samples, histogram
# -------------------------------------------
bounds['acq_y'] = (0.0, 10.0)
ax2_title = r'Frequency of $\lambda=\hat{\lambda}^*$'
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True)
ax1.set_xlim(bounds['x'])
ax1.set_ylim(bounds['gp_y'])
ax2.set_xlim(bounds['x'])
ax2.set_ylim(bounds['acq_y'])
ax1.grid()
ax2.grid()
boplot.plot_objective_function(ax=ax1)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=False, ax=ax1)
nsamples = 10
draw_samples(nsamples=nsamples, ax1=ax1, ax2=ax2)
ax1.set_xlabel(labels['xlabel'])
ax1.set_ylabel(labels['gp_ylabel'])
ax1.set_title(r"Ten samples from $\mathcal{G}^t$", loc='left')
ax2.set_xlabel(labels['xlabel'])
# ax2.set_ylabel(r'$p_{min}$')
ax2.set_ylabel(r'Frequency')
ax2.set_title(ax2_title, loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig('es_3')
else:
plt.show()
# -------------------------------------------
# 4. Show GP fit on initial dataset, 200 samples, histogram
# -------------------------------------------
bounds["acq_y"] = (0.0, 20.0)
ax2_title = r'Frequency of $\lambda=\hat{\lambda}^*$'
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True)
ax1.set_xlim(bounds['x'])
ax1.set_ylim(bounds['gp_y'])
ax2.set_xlim(bounds['x'])
ax2.set_ylim(bounds['acq_y'])
ax1.grid()
ax2.grid()
boplot.plot_objective_function(ax=ax1)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=False, ax=ax1)
nsamples = 200
draw_samples(nsamples=nsamples, ax1=ax1, ax2=ax2)
ax1.set_xlabel(labels['xlabel'])
ax1.set_ylabel(labels['gp_ylabel'])
ax1.set_title(r"200 samples from $\mathcal{G}^t$", loc='left')
ax2.set_xlabel(labels['xlabel'])
# ax2.set_ylabel(r'$p_{min}$')
ax2.set_ylabel(r'Frequency')
ax2.set_title(ax2_title, loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig('es_4')
else:
plt.show()
# -------------------------------------------
# 5. Show PDF derived from the histogram at 200 samples
# -------------------------------------------
ax2_title = "$\hat{P}(\lambda=\lambda^*)$"
bounds["acq_y"] = (0.0, 1.0)
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True)
ax1.set_xlim(bounds['x'])
ax1.set_ylim(bounds['gp_y'])
ax2.set_xlim(bounds['x'])
ax2.set_ylim(bounds["acq_y"])
ax1.grid()
ax2.grid()
boplot.plot_objective_function(ax=ax1)
boplot.plot_gp(model=gp, confidence_intervals=[3.0], ax=ax1, custom_x=x)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=False, ax=ax1)
nsamples = 200
seed3 = 65
mu = gp.sample_y(X=X_, n_samples=nsamples, random_state=seed3)
data_h = X_[np.argmin(mu, axis=0), 0]
kde = kd(kernel='gaussian', bandwidth=0.75).fit(data_h.reshape(-1, 1))
xplot = boplot.get_plot_domain()
ys = np.exp(kde.score_samples(xplot))
ax2.plot(xplot, ys, color='green', lw=2.)
ax2.fill_between(xplot[:, 0], ax2.get_ylim()[0], ys, color='lightgreen')
ax1.set_xlabel(labels['xlabel'])
ax1.set_ylabel(labels['gp_ylabel'])
ax1.set_title(r"Visualization of $\mathcal{G}^t$", loc='left')
ax2.set_xlabel(labels['xlabel'])
ax2.set_ylabel(r'$p_{min}$')
ax2.set_title(ax2_title, loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig('es_5')
else:
plt.show()
# -------------------------------------------
# 6. Mark maximum of the PDF as next configuration to be evaluated
# -------------------------------------------
ax2_title = "$\hat{P}(\lambda=\lambda^*)$"
bounds["acq_y"] = (0.0, 1.0)
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True)
ax1.set_xlim(bounds['x'])
ax1.set_ylim(bounds['gp_y'])
ax2.set_xlim(bounds['x'])
ax2.set_ylim(bounds["acq_y"])
ax1.grid()
ax2.grid()
boplot.plot_objective_function(ax=ax1)
boplot.plot_gp(model=gp, confidence_intervals=[3.0], ax=ax1, custom_x=x)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=False, ax=ax1)
nsamples = 200
seed3 = 65
mu = gp.sample_y(X=X_, n_samples=nsamples, random_state=seed3)
data_h = X_[np.argmin(mu, axis=0), 0]
kde = kd(kernel='gaussian', bandwidth=0.75).fit(data_h.reshape(-1, 1))
xplot = boplot.get_plot_domain()
ys = np.exp(kde.score_samples(xplot))
idx_umax = np.argmax(ys)
boplot.highlight_configuration(x=xplot[idx_umax],
label='',
ax=ax1,
disable_ticks=True)
boplot.annotate_x_edge(label=r'$\lambda^{(t)}$',
xy=(xplot[idx_umax], ax1.get_ylim()[0]),
ax=ax1,
align='top',
offset_param=1.5)
boplot.highlight_configuration(x=xplot[idx_umax],
label='',
ax=ax2,
disable_ticks=True)
boplot.annotate_x_edge(label=r'$\lambda^{(t)}$',
xy=(xplot[idx_umax], ys[idx_umax]),
ax=ax2,
align='top',
offset_param=1.0)
ax2.plot(xplot, ys, color='green', lw=2.)
ax2.fill_between(xplot[:, 0], ax2.get_ylim()[0], ys, color='lightgreen')
ax1.set_xlabel(labels['xlabel'])
ax1.set_ylabel(labels['gp_ylabel'])
ax1.set_title(r"Visualization of $\mathcal{G}^t$", loc='left')
ax2.set_xlabel(labels['xlabel'])
ax2.set_ylabel(r'$p_{min}$')
ax2.set_title(ax2_title, loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig('es_6')
else:
plt.show()
def visualize_ts(initial_design, init=None):
"""
Visualize one-step of TS.
:param initial_design: Method for initializing the GP, choice between 'uniform', 'random', and 'presentation'
:param init: Number of datapoints to initialize GP with.
:return: None
"""
# 1. Plot GP fit on initial dataset
# 2. Plot one sample
# 3. Mark minimum of sample
boplot.set_rcparams(**{'legend.loc': 'upper left'})
logging.debug("Visualizing EI with initial design {} and init {}".format(
initial_design, init))
# Initialize dummy dataset
x, y = initialize_dataset(initial_design=initial_design, init=init)
ymin_arg = np.argmin(y)
ymin = y[ymin_arg]
logging.debug(
"Initialized dataset with:\nsamples {0}\nObservations {1}".format(
x, y))
# Fit GP to the currently available dataset
gp = GPR(kernel=Matern())
logging.debug("Fitting GP to\nx: {}\ny:{}".format(x, y))
gp.fit(x, y) # fit the model
# noinspection PyStringFormat
logging.debug(
"Model fit to dataset.\nOriginal Inputs: {0}\nOriginal Observations: {1}\n"
"Predicted Means: {2}\nPredicted STDs: {3}".format(
x, y, *(gp.predict(x, return_std=True))))
# 1. Plot GP fit on initial dataset
# -------------Plotting code -----------------
fig, ax = plt.subplots(1, 1, squeeze=True)
ax.set_xlim(bounds["x"])
ax.set_ylim(bounds["gp_y"])
ax.grid()
boplot.plot_gp(model=gp,
confidence_intervals=[1.0, 2.0, 3.0],
custom_x=x,
ax=ax)
boplot.plot_objective_function(ax=ax)
boplot.mark_observations(X_=x,
Y_=y,
mark_incumbent=False,
highlight_datapoint=None,
highlight_label=None,
ax=ax)
ax.legend().set_zorder(zorders['legend'])
ax.set_xlabel(labels['xlabel'])
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig(f"{OUTPUT_DIR}/ts_1.pdf")
else:
plt.show()
# -------------------------------------------
# 2. Plot one sample
# -------------Plotting code -----------------
fig, ax = plt.subplots(1, 1, squeeze=True)
ax.set_xlim(bounds["x"])
ax.set_ylim(bounds["gp_y"])
ax.grid()
boplot.plot_gp(model=gp,
confidence_intervals=[1.0, 2.0, 3.0],
custom_x=x,
ax=ax)
boplot.plot_objective_function(ax=ax)
boplot.mark_observations(X_=x,
Y_=y,
mark_incumbent=False,
highlight_datapoint=None,
highlight_label=None,
ax=ax)
# Sample from the GP
nsamples = 1
seed2 = 2
seed3 = 1375
X_ = boplot.get_plot_domain(precision=None)
mu = gp.sample_y(X=X_, n_samples=nsamples, random_state=seed3)
boplot.plot_gp_samples(mu=mu,
nsamples=nsamples,
precision=None,
custom_x=X_,
show_min=False,
ax=ax,
seed=seed2)
ax.legend().set_zorder(zorders['legend'])
ax.set_xlabel(labels['xlabel'])
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig(f"{OUTPUT_DIR}/ts_2.pdf")
else:
plt.show()
# -------------------------------------------
# 3. Mark minimum of sample
# -------------Plotting code -----------------
fig, ax = plt.subplots(1, 1, squeeze=True)
ax.set_xlim(bounds["x"])
ax.set_ylim(bounds["gp_y"])
ax.grid()
# boplot.plot_gp(model=gp, confidence_intervals=[2.0], custom_x=x, ax=ax)
boplot.plot_objective_function(ax=ax)
boplot.mark_observations(X_=x,
Y_=y,
mark_incumbent=False,
highlight_datapoint=None,
highlight_label=None,
ax=ax)
# Sample from the GP
nsamples = 1
X_ = boplot.get_plot_domain(precision=None)
mu = gp.sample_y(X=X_, n_samples=nsamples, random_state=seed3)
colors['highlighted_observations'] = 'red' # Special for Thompson Sampling
boplot.plot_gp_samples(mu=mu,
nsamples=nsamples,
precision=None,
custom_x=X_,
show_min=True,
ax=ax,
seed=seed2)
min_idx = np.argmin(mu, axis=0)
candidate = X_[min_idx]
cost = mu[min_idx]
boplot.highlight_configuration(x=np.array([candidate]),
label='',
lloc='bottom',
disable_ticks=True,
ax=ax)
boplot.annotate_x_edge(label=r'$\lambda^{(t)}$',
xy=(candidate, cost),
align='top',
ax=ax)
boplot.highlight_output(y=np.array([cost]),
label='',
lloc='left',
disable_ticks=True,
ax=ax)
boplot.annotate_y_edge(label=r'$g(\lambda^{(t)})$',
xy=(candidate, cost),
align='left',
ax=ax)
ax.legend().set_zorder(zorders['legend'])
ax.set_xlabel(labels['xlabel'])
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig(f"{OUTPUT_DIR}/ts_3.pdf")
else:
plt.show()