def get_mu_star(model):
"""
Given a model, return the (x, y) coords of mu-star.
:param model: The underlying GP.
:return:
"""
X_ = boplot.get_plot_domain()
mu = model.predict(X_).reshape(-1, 1)
coords = np.hstack((X_, mu))
idx = np.argmin(coords, axis=0)[1]
return coords[idx, :]
def get_lcb_maximum(model, kappa):
"""
Given a model, return the (x, y) coords of LCB maximum.
:param model: The underlying GP.
:return:
"""
X_ = boplot.get_plot_domain()
mu, sigma = model.predict(X_, return_std=True)
lcb = - (mu - kappa * sigma).reshape(-1)
idx = np.argmax(lcb)
logging.info("Found lcb maximum at index {}:{}".format(idx, (X_[idx, 0], lcb[idx])))
return (X_[idx, 0], -lcb[idx])
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 10e9 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_rc('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 bin_large_sample_size(nsamples,
seed,
return_pdf=False,
batch_size=1280000):
# Used for plotting a histogram when a large number of samples are to be generated.
logging.info(
f"Generating batch-wise histogram data for {nsamples} samples {batch_size} samples at at time."
)
rng = np.random.RandomState(seed=seed)
counts = np.zeros_like(X_.flatten())
bin_edges = np.zeros(shape=(counts.shape[0] + 1))
# Smoothen out the batches - we don't care about missing out a small overflow number of samples.
nsamples = (nsamples // batch_size) * batch_size
for idx in range(0, nsamples, batch_size):
# # Iterate in increments of batch_size samples, but check for an uneven batch in the last iteration
# batch_nsamples = batch_size if (nsamples - idx) % batch_size == 0 else nsamples - idx
if idx % (batch_size * 10) == 0:
logging.info(
f"Generated {idx} samples out of an expected {nsamples}"
f"[{idx * 100.0 / nsamples}%].")
batch_nsamples = batch_size
mu = gp.sample_y(X=X_, n_samples=batch_nsamples, random_state=rng)
minima = X_[np.argmin(mu, axis=0), 0]
hist, bin_edges = np.histogram(
minima,
bins=nbins,
range=bin_range,
density=return_pdf,
)
counts += hist
logging.info(f"Finished generating {nsamples} samples.")
return counts, bin_edges
def draw_samples(nsamples,
ax1,
ax2,
show_min=False,
return_pdf=False,
show_samples=True,
show_hist=True,
data=None):
if not nsamples:
raise RuntimeError(
f"Number of samples must be a positive integer, received "
f"{nsamples} of type {type(nsamples)}")
# If data is not None, assume that it contains pre-computed histogram data
logging.debug("Recieved histogram data of shape %s." %
str(np.array(data).shape))
if data:
logging.debug(
"Histogram data contained %d counts and %d bins." %
(np.array(data[0]).shape[0], np.array(data[1]).shape[0]))
counts = data[0]
bins = data[1]
return ax2.hist(bins[:-1],
bins=bins,
density=return_pdf,
weights=counts,
color='lightgreen',
edgecolor='black',
alpha=0.0 if return_pdf else 1.0)
mu = gp.sample_y(X=X_, n_samples=nsamples, random_state=GP_SAMPLE_SEED)
if show_samples:
boplot.plot_gp_samples(mu=mu,
nsamples=nsamples,
precision=histogram_precision,
custom_x=X_,
show_min=show_min,
ax=ax1,
seed=GP_SAMPLE_COLOR_SEED)
minima = X_[np.argmin(mu, axis=0), 0]
logging.info("Shape of minima is {}".format(minima.shape))
# logging.debug("minima is: {}".format(minima))
bins = None
if show_hist:
bins = ax2.hist(minima,
bins=nbins,
range=bin_range,
density=return_pdf,
color='lightgreen',
edgecolor='black',
alpha=0.0 if return_pdf else 1.0)
return bins
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 draw_freq_plots(nsamples):
fig, (ax1, ax2) = draw_basic_plot()
if nsamples == 1:
show_min = True
else:
show_min = False
draw_samples(nsamples=nsamples, ax1=ax1, ax2=ax2, show_min=show_min)
return fig, (ax1, ax2)
def finishing_touches(ax1,
ax2,
ax1_title,
ax2_title,
show_legend=False,
figname="es.pdf"):
ax1.set_xlabel(labels['xlabel'])
# ax1.set_ylabel(labels['gp_ylabel'])
# ax1.set_title(ax1_title, loc='left')
ax2.set_xlabel(labels['xlabel'])
ax2.set_ylabel(r'Frequency')
ax2.set_title(ax2_title, loc='left')
if show_legend:
ax1.legend().set_zorder(zorders["legend"])
else:
ax1.legend().remove()
# plt.tight_layout()
plt.subplots_adjust(hspace=1.0)
if TOGGLE_PRINT:
plt.savefig(f"{OUTPUT_DIR}/{figname}")
else:
plt.show()
# 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) = draw_basic_plot()
boplot.plot_gp(model=gp,
confidence_intervals=[1.0, 2.0, 3.0],
ax=ax1,
custom_x=x)
# 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(zorders["legend"])
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(f"{OUTPUT_DIR}/es_1.pdf")
else:
plt.show()
# -------------------------------------------
# 2. Show GP fit on initial dataset, 1 sample, histogram
# -------------------------------------------
nsamples = 1
bounds['acq_y'] = (0.0, 5.0)
ax1_title = r"One sample$"
ax2_title = r'Frequency of $\lambda=\hat{\lambda}^*$'
figname = "es_2.pdf"
fig, (ax1, ax2) = draw_freq_plots(nsamples=nsamples)
finishing_touches(ax1=ax1,
ax2=ax2,
ax1_title=ax1_title,
ax2_title=ax2_title,
show_legend=False,
figname=figname)
# 3. Show GP fit on initial dataset, 10 samples, histogram
# -------------------------------------------
nsamples = 10
bounds['acq_y'] = (0.0, 10.0)
ax1_title = r"Ten samples$"
ax2_title = r'Frequency of $\lambda=\hat{\lambda}^*$'
figname = "es_3.pdf"
fig, (ax1, ax2) = draw_freq_plots(nsamples=nsamples)
finishing_touches(ax1=ax1,
ax2=ax2,
ax1_title=ax1_title,
ax2_title=ax2_title,
show_legend=False,
figname=figname)
# -------------------------------------------
# 4. Show GP fit on initial dataset, 200 samples, histogram
# -------------------------------------------
nsamples = 100
bounds["acq_y"] = (0.0, 20.0)
ax1_title = r"200 samples$"
ax2_title = r'Frequency of $\lambda=\hat{\lambda}^*$'
figname = "es_4.pdf"
fig, (ax1, ax2) = draw_freq_plots(nsamples=nsamples)
finishing_touches(ax1=ax1,
ax2=ax2,
ax1_title=ax1_title,
ax2_title=ax2_title,
show_legend=False,
figname=figname)
# -------------------------------------------
# 5. Show PDF derived from the histogram at 10e9 samples
# -------------------------------------------
nsamples = int(1e9) # Generate ~1 Billion samples
bounds["acq_y"] = (0.0, nsamples / 10.0)
# ax1_title = r"200 samples from $\mathcal{G}^t$"
# ax2_title = "$\hat{P}(\lambda=\lambda^*)$"
ax1_title = r"A very large number of samples"
ax2_title = r'Frequency of $\lambda=\hat{\lambda}^*$'
figname = "es_5.pdf"
fig, (ax1, ax2) = draw_basic_plot(ax2_sci_not=True)
# Draw only a limited number of samples
draw_samples(nsamples=200,
ax1=ax1,
ax2=ax2,
show_min=False,
show_samples=True,
show_hist=False)
# Use an alternate procedure to generate the histogram data
counts, bins = bin_large_sample_size(nsamples,
seed=GP_SAMPLE_SEED,
return_pdf=False,
batch_size=1280000)
hist_data = (counts, bins)
# Draw histogram only for a large number of samples
draw_samples(nsamples=nsamples,
ax1=ax1,
ax2=ax2,
show_min=False,
show_samples=False,
show_hist=True,
data=hist_data)
finishing_touches(ax1=ax1,
ax2=ax2,
ax1_title=ax1_title,
ax2_title=ax2_title,
show_legend=False,
figname=figname)
# -------------------------------------------
# 6. Mark maximum of the PDF as next configuration to be evaluated
# -------------------------------------------
figname = "es_6.pdf"
fig, (ax1, ax2) = draw_basic_plot(ax2_sci_not=True)
# Draw only a limited number of samples
draw_samples(nsamples=200,
ax1=ax1,
ax2=ax2,
show_min=False,
show_samples=True,
show_hist=False)
# Draw histogram only for a large number of samples using previously generated histogram data
draw_samples(nsamples=nsamples,
ax1=ax1,
ax2=ax2,
show_min=False,
show_samples=False,
show_hist=True,
data=hist_data)
xplot = boplot.get_plot_domain()
idx_umax = np.argmax(counts)
xmax = (bins[idx_umax] + bins[idx_umax + 1]) / 2.0
logging.info(
f"Highlighting xmax as configuration at index {idx_umax} with count {counts[idx_umax]}, "
f"at configuration {xmax}.")
boplot.highlight_configuration(x=xmax,
label=r'$\lambda^{(t)}$',
ax=ax1,
disable_ticks=False)
# 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=xmax,
label=r'$\lambda^{(t)}$',
ax=ax2,
disable_ticks=False)
# boplot.annotate_x_edge(label=r'$\lambda^{(t)}$', xy=(xplot[idx_umax], ys[idx_umax]),
# ax=ax2, align='top', offset_param=1.0)
finishing_touches(ax1=ax1,
ax2=ax2,
ax1_title=ax1_title,
ax2_title=ax2_title,
show_legend=False,
figname=figname)
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()
