def draw_basic_figure(obj_func=False):
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)
if obj_func:
boplot.plot_objective_function(ax=ax)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.highlight_output(
y=np.array([ymin]),
label=['$c_{inc}$'],
lloc='left',
ax=ax,
# disable_ticks=True
)
# boplot.annotate_y_edge(
# label='$c_{inc}$',
# xy=((ax.get_xlim()[0] + x[ymin_arg]) / 2, ymin),
# ax=ax,
# align='left',
# yoffset=1.0
# )
return fig, ax
def draw_basic_plot(mark_incumbent=True, show_objective=False):
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)
if show_objective:
boplot.plot_objective_function(ax=ax)
boplot.mark_observations(X_=x,
Y_=y,
mark_incumbent=mark_incumbent,
highlight_datapoint=None,
highlight_label=None,
ax=ax)
if mark_incumbent:
boplot.highlight_output(y=np.array([ymin]),
label=['$c_{inc}$'],
lloc='left',
ax=ax,
disable_ticks=True)
boplot.annotate_y_edge(label='$c_{inc}$',
xy=(x[ymin_arg], ymin),
ax=ax,
align='left',
yoffset=0.8)
return fig, ax
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 draw_basic_plot(ax2_sci_not=False):
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'])
if ax2_sci_not:
f = mtick.ScalarFormatter(useOffset=False, useMathText=True)
g = lambda x, pos: "${}$".format(f._formatSciNotation('%1.10e' % x)
)
ax2.yaxis.set_major_formatter(mtick.FuncFormatter(g))
ax1.grid()
ax2.grid()
boplot.plot_objective_function(ax=ax1)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=False, ax=ax1)
return fig, (ax1, ax2)
def run_bo(acquisition, max_iter, initial_design, acq_add, init=None):
"""
BO
:param acquisition: type of acquisition function to be used
:param max_iter: max number of function calls
:param seed: seed used to keep experiments reproducible
:param initial_design: Method for initializing the GP, choice between 'uniform', 'random', and 'presentation'
:param acq_add: additional parameteres for acquisition function (e.g. kappa for LCB)
:param init: Number of datapoints to initialize GP with.
:return: all evaluated points.
"""
logging.debug("Running BO with Acquisition Function {0}, maximum iterations {1}, initial design {2}, "
"acq_add {3} and init {4}".format(acquisition, max_iter, initial_design, acq_add, init))
x, y = initialize_dataset(initial_design=initial_design, init=init)
logging.debug("Initialized dataset with:\nsamples {0}\nObservations {1}".format(x, y))
for i in range(1, max_iter): # BO loop
logging.debug('Sample #%d' % (i))
# 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
# ----------Plotting calls---------------
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True)
fig.tight_layout()
ax1.set_xlim(bounds["x"])
ax1.set_ylim(bounds["gp_y"])
ax1.grid()
ax2.set_xlim(bounds["x"])
ax2.set_ylim(bounds["acq_y"])
ax2.grid()
boplot.plot_objective_function(ax=ax1)
boplot.plot_gp(model=gp, confidence_intervals=[1.0, 2.0], ax=ax1, custom_x=x)
boplot.mark_observations(X_=x, Y_=y, ax=ax1)
# ---------------------------------------
# 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))))
# Partially initialize the acquisition function to work with the fmin interface
# (only the x parameter is not specified)
acqui = partial(acquisition, model=gp, eta=min(y), add=acq_add)
boplot.plot_acquisition_function(acquisition, min(y), gp, acq_add, invert=True, ax=ax2)
# optimize acquisition function, repeat 10 times, use best result
x_ = None
y_ = 10000
# Feel free to adjust the hyperparameters
for j in range(NUM_ACQ_OPTS):
opt_res = minimize(acqui, np.random.uniform(bounds["x"][0], bounds["x"][1]),
#bounds=bounds["x"],
options={'maxfun': 20, 'maxiter': 20}, method="L-BFGS-B")
if opt_res.fun[0] < y_:
x_ = opt_res.x
y_ = opt_res.fun[0]
# ----------Plotting calls---------------
boplot.highlight_configuration(x_, ax=ax1)
boplot.highlight_configuration(x_, ax=ax2)
# ---------------------------------------
# Update dataset with new observation
x.append(x_)
y.append(f(x_))
logging.info("After {0}. loop iteration".format(i))
logging.info("x: {0:.3E}, y: {1:.3E}".format(x_[0], y_))
# ----------Plotting calls---------------
for ax in (ax1, ax2):
ax.legend()
ax.set_xlabel(labels['xlabel'])
ax1.set_ylabel(labels['gp_ylabel'])
ax1.set_title("Visualization of GP", loc='left')
ax2.set_title("Visualization of Acquisition Function", loc='left')
ax2.set_ylabel(labels['acq_ylabel'])
if TOGGLE_PRINT:
plt.savefig("plot_{}.pdf".format(i), dpi='figure')
else:
plt.show()
# ---------------------------------------
return y
def visualize_ei(initial_design, init=None):
"""
Visualize one-step of EI.
: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. Mark current incumbent
# 3. Mark Zone of Probable Improvement
# 4. Mark Hypothetical Real cost of a random configuration
# 5. Display I(lambda)
# 6. Display Vertical Normal Distribution
# boplot.set_rcparams(**{'legend.loc': 'lower 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
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))))
# 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=[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)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("ei_1.pdf")
else:
plt.show()
# -------------------------------------------
# 2. Mark current incumbent
# -------------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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("ei_2.pdf")
else:
plt.show()
# -------------------------------------------
# 3. Mark Zone of Probable Improvement
# -------------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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.darken_graph(y=ymin, ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
ax.legend().remove()
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("ei_3.pdf")
else:
plt.show()
# -------------------------------------------
# 4. Forget the underlying objective function
# -------------------------------------------
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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.darken_graph(y=ymin, ax=ax)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("ei_4.pdf")
else:
plt.show()
# -------------------------------------------
# 5. Mark Hypothetical Real cost of a random configuration
# -------------------------------------------
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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.darken_graph(y=ymin, ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
candidate = 11.0
cost = -3.5
boplot.highlight_configuration(x=np.array([candidate]), label=r'$\lambda$', lloc='bottom', ax=ax)
boplot.highlight_output(y=np.array([cost]), label='', lloc='left', ax=ax)
boplot.annotate_y_edge(label=r'$c(\lambda)$', xy=(candidate, cost), align='left', ax=ax)
ax.legend().remove()
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("ei_5.pdf")
else:
plt.show()
# -------------------------------------------
# 6. Display I(lambda)
# -------------------------------------------
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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.darken_graph(y=ymin, ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
boplot.highlight_configuration(x=np.array([candidate]), label=r'$\lambda$', lloc='bottom', ax=ax)
boplot.highlight_output(y=np.array([cost]), label='', lloc='left', ax=ax)
boplot.annotate_y_edge(label=r'$c(\lambda)$', xy=(candidate, cost), align='left', ax=ax)
xmid = (x[ymin_arg][0] + candidate) / 2.
ax.annotate(s='', xy=(xmid, cost), xytext=(xmid, ymin),
arrowprops={'arrowstyle': '<|-|>',})
textx = xmid + (ax.get_xlim()[1] - ax.get_xlim()[0]) / 40
ax.text(textx, (ymin + cost) / 2, r'$I^{(t)}(\lambda)$', weight='heavy')
ax.legend().remove()
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("ei_6.pdf")
else:
plt.show()
# -------------------------------------------
# 7. Display Vertical Normal Distribution
# -------------------------------------------
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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.darken_graph(y=ymin, ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
boplot.highlight_configuration(x=np.array([candidate]), label=r'$\lambda$', lloc='bottom', ax=ax)
boplot.highlight_output(y=np.array([cost]), label='', lloc='left', ax=ax)
boplot.annotate_y_edge(label=r'$c(\lambda)$', xy=(candidate, cost), align='left', ax=ax)
xmid = (x[ymin_arg][0] + candidate) / 2.
ax.annotate(s='', xy=(xmid, cost), xytext=(xmid, ymin),
arrowprops={'arrowstyle': '<|-|>',})
textx = xmid + (ax.get_xlim()[1] - ax.get_xlim()[0]) / 40
ax.text(textx, (ymin + cost) / 2, r'$I^{(t)}(\lambda)$', weight='heavy')
vcurve_x, vcurve_y, mu = boplot.draw_vertical_normal(
gp=gp, incumbenty=ymin, ax=ax, xtest=candidate,
xscale=2.0, yscale=1.0
)
ann_x = candidate + 0.3 * (np.max(vcurve_x) - candidate) / 2
ann_y = ymin - (mu - ymin) / 2
arrow_x = ann_x + 0.5
arrow_y = ann_y - 3.0
# label = "{:.2f}".format(candidate)
label = '\lambda'
ax.annotate(
s=r'$PI^{(t)}(%s)$' % label, xy=(ann_x, ann_y), xytext=(arrow_x, arrow_y),
arrowprops={'arrowstyle': 'fancy'},
weight='heavy', color='darkgreen', zorder=15
)
ax.legend().remove()
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("ei_7.pdf")
else:
plt.show()
def visualize_lcb(initial_design, init=None):
"""
Visualize one-step of LCB.
: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 3 different confidence envelopes
# 2. Plot only LCB for one confidence envelope
# 3. Mark next sample
boplot.set_rcparams(**{'legend.loc': 'lower right'})
logging.debug("Visualizing PI 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
# 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 3 different confidence envelopes
# -------------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], type='both', 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}/lcb_1.pdf")
else:
plt.show()
# -------------------------------------------
# 2. Plot only LCB for one confidence envelope
# -------------Plotting code -----------------
boplot.set_rcparams(**{'legend.loc': 'upper left'})
kappa = 3.0
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=[kappa], type='lower', custom_x=x, ax=ax)
boplot.plot_objective_function(ax=ax)
boplot.mark_observations(X_=x, Y_=y, mark_incumbent=True, 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}/lcb_2.pdf")
else:
plt.show()
def visualize_lcb(initial_design, init=None):
"""
Visualize one-step of LCB.
: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 3 different confidence envelopes
# 2. Plot only LCB for one confidence envelope
# 3. Mark next sample
boplot.set_rcparams(**{'legend.loc': 'lower right'})
logging.debug("Visualizing PI 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
# 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 3 different confidence envelopes
# -------------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],
type='both',
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(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("lcb_1.pdf")
else:
plt.show()
# -------------------------------------------
# 2. Plot only LCB for one confidence envelope
# -------------Plotting code -----------------
boplot.set_rcparams(**{'legend.loc': 'upper left'})
kappa = 3.0
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=[kappa],
type='lower',
custom_x=x,
ax=ax)
boplot.plot_objective_function(ax=ax)
boplot.mark_observations(X_=x,
Y_=y,
mark_incumbent=True,
highlight_datapoint=None,
highlight_label=None,
ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("lcb_2.pdf")
else:
plt.show()
# -------------------------------------------
# 3. Show LCB in parallel
# -------------Plotting code -----------------
if TOGGLE_PRINT:
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True, figsize=(18, 9))
else:
fig, (ax1, ax2) = plt.subplots(2, 1, squeeze=True)
ax1.set_xlim(bounds["x"])
ax1.set_ylim(bounds["gp_y"])
ax1.grid()
boplot.plot_gp(model=gp,
confidence_intervals=[kappa],
type='lower',
custom_x=x,
ax=ax1)
boplot.plot_objective_function(ax=ax1)
boplot.mark_observations(X_=x,
Y_=y,
mark_incumbent=True,
highlight_datapoint=None,
highlight_label=None,
ax=ax1)
lcb_max = get_lcb_maximum(gp, kappa)
logging.info("LCB Maximum at:{}".format(lcb_max))
boplot.highlight_configuration(x=lcb_max[0],
label=None,
lloc='bottom',
ax=ax1)
ax1.set_xlabel(labels['xlabel'])
ax1.set_ylabel(labels['gp_ylabel'])
ax1.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
ax2.set_xlim(bounds["x"])
ax2.set_ylim(bounds["acq_y"])
ax2.grid()
ax2.set_xlabel(labels['xlabel'])
ax2.set_ylabel(labels['acq_ylabel'])
ax2.set_title(r"Visualization of $LCB$", loc='left')
boplot.highlight_configuration(x=lcb_max[0],
label=None,
lloc='bottom',
ax=ax2)
boplot.plot_acquisition_function(acquisition_functions['LCB'],
0.0,
gp,
kappa,
ax=ax2)
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("lcb_3.pdf")
else:
plt.show()
# -------------------------------------------
# 4. Mark next 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=[3.0],
type='lower',
custom_x=x,
ax=ax)
boplot.plot_objective_function(ax=ax)
boplot.mark_observations(X_=x,
Y_=y,
mark_incumbent=True,
highlight_datapoint=None,
highlight_label=None,
ax=ax)
lcb_max = get_lcb_maximum(gp, 3.0)
logging.info("LCB Maximum at:{}".format(lcb_max))
boplot.highlight_configuration(x=lcb_max[0],
label=None,
lloc='bottom',
ax=ax)
boplot.highlight_output(y=lcb_max[1], label='', lloc='left', ax=ax)
boplot.annotate_y_edge(label=r'${\hat{c}}^{(t)}(%.2f)$' % lcb_max[0],
xy=lcb_max,
align='left',
ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
ax.legend().remove()
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("lcb_4.pdf")
else:
plt.show()
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_pi(initial_design, init=None):
"""
Visualize one-step of PI.
: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. Mark current incumbent
# 3. Mark Zone of Probable Improvement
# 4. Draw Vertical Normal at a good candidate for improvement
# 5. Draw Vertical Normal at a bad candidate for improvement
# boplot.set_rcparams(**{'legend.loc': 'lower left'})
logging.debug("Visualizing PI 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
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))))
# 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=[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)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("pi_1.pdf")
else:
plt.show()
# -------------------------------------------
# 2. Mark current incumbent
# -------------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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("pi_2.pdf")
else:
plt.show()
# -------------------------------------------
# 3. Mark Zone of Probable Improvement
# -------------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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.darken_graph(y=ymin, ax=ax)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
ax.legend().remove()
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("pi_3.pdf")
else:
plt.show()
# -------------------------------------------
# 4. Draw Vertical Normal at a good candidate for improvement
# -------------Plotting code -----------------
candidate = 5.0
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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.darken_graph(y=ymin, ax=ax)
vcurve_x, vcurve_y, mu = boplot.draw_vertical_normal(gp=gp, incumbenty=ymin, ax=ax, xtest=candidate, xscale=2.0, yscale=1.0)
ann_x = candidate + 0.5 * (np.max(vcurve_x) - candidate) / 2
ann_y = mu - 0.25
arrow_x = ann_x
arrow_y = ann_y - 3.0
label = "{:.2f}".format(candidate)
ax.annotate(
s=r'$PI^{(t)}(%s)$' % label, xy=(ann_x, ann_y), xytext=(arrow_x, arrow_y),
arrowprops={'arrowstyle': 'fancy'},
weight='heavy', color='darkgreen', zorder=15
)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
ax.legend().remove()
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("pi_4.pdf")
else:
plt.show()
# -------------------------------------------
# 5. Draw Vertical Normal at a bad candidate for improvement
# -------------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=True, highlight_datapoint=None, highlight_label=None, ax=ax)
boplot.darken_graph(y=ymin, ax=ax)
candidate = 5.0
vcurve_x, vcurve_y, mu = boplot.draw_vertical_normal(
gp=gp, incumbenty=ymin, ax=ax, xtest=candidate,
xscale=2.0, yscale=1.0
)
ann_x = candidate + 0.5 * (np.max(vcurve_x) - candidate) / 2
ann_y = mu - 0.25
arrow_x = ann_x
arrow_y = ann_y - 3.0
label = "{:.2f}".format(candidate)
ax.annotate(
s=r'$PI^{(t)}(%s)$' % label, xy=(ann_x, ann_y), xytext=(arrow_x, arrow_y),
arrowprops={'arrowstyle': 'fancy'},
weight='heavy', color='darkgreen', zorder=15
)
candidate = 8.0
vcurve_x, vcurve_y, mu = boplot.draw_vertical_normal(
gp=gp, incumbenty=ymin, ax=ax, xtest=candidate,
xscale=2.0, yscale=1.0
)
ann_x = candidate + 0.5 * (np.max(vcurve_x) - candidate) / 2
ann_y = mu
arrow_x = ann_x
arrow_y = ann_y - 3.0
label = "{:.2f}".format(candidate)
ax.annotate(s=r'$PI^{(t)}(%s)$' % label, xy=(ann_x, ann_y), xytext=(arrow_x, arrow_y),
arrowprops={'arrowstyle': 'fancy'},
weight='heavy', color='darkgreen', zorder=15)
ax.legend().set_zorder(20)
ax.set_xlabel(labels['xlabel'])
ax.set_ylabel(labels['gp_ylabel'])
ax.set_title(r"Visualization of $\mathcal{G}^{(t)}$", loc='left')
ax.legend().remove()
plt.tight_layout()
if TOGGLE_PRINT:
plt.savefig("pi_5.pdf")
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()