def plot(self):
"""
Plot performance over time, using all trajectory entries.
max_time denotes max(wallclock_limit, highest recorded time).
"""
rh, runs, output_fn, validator = self.rh, self.runs, self.output_fn, self.validator
# Add lines to be plotted to lines (key-values must be zippable)
lines = []
# Get plotting data and create CDS
if self.bohb_results:
lines.append(self._get_bohb_line(validator, runs, rh))
for b in self.bohb_results[0].HB_config['budgets']:
lines.append(self._get_bohb_line(validator, runs, rh, b))
else:
lines.append(self._get_avg(validator, runs, rh))
lines.extend(self._get_all_runs(validator, runs, rh))
data = {'name': [], 'time': [], 'mean': [], 'upper': [], 'lower': []}
hp_names = self.scenario.cs.get_hyperparameter_names()
for p in hp_names:
data[p] = []
for line in lines:
for t, m, u, l, c in zip(line.time, line.mean, line.upper,
line.lower, line.config):
if not (np.isfinite(m) and np.isfinite(u) and np.isfinite(l)):
self.logger.debug("Why is there a NaN? (%s)", str(line))
raise ValueError(
"There is a NaN value in your data, this should be filtered out. "
"Please report this to github.com/automl/CAVE/issues and provide the "
"debug/debug.log and the output of `pip freeze`, if you can."
)
data['name'].append(line.name)
data['time'].append(t)
data['mean'].append(m)
data['upper'].append(u)
data['lower'].append(l)
for p in hp_names:
data[p].append(c[p] if (c and p in c) else 'inactive')
source = ColumnDataSource(data=data)
# Create plot
x_range = Range1d(min(source.data['time']), max(source.data['time']))
y_label = 'estimated {}'.format(self.scenario.run_obj if self.scenario.
run_obj != 'quality' else 'cost')
p = figure(plot_width=700,
plot_height=500,
tools=['save', 'pan', 'box_zoom', 'wheel_zoom', 'reset'],
x_range=x_range,
x_axis_type='log',
y_axis_type='log'
if self.scenario.run_obj == 'runtime' else 'linear',
x_axis_label='time (sec)',
y_axis_label=y_label,
title="Cost over time")
colors = itertools.cycle(Dark2_5)
renderers = []
legend_it = []
for line, color in zip(lines, colors):
# CDSview w GroupFilter
name = line.name
view = CDSView(
source=source,
filters=[GroupFilter(column_name='name', group=str(name))])
renderers.append([
p.line('time',
'mean',
source=source,
view=view,
line_color=color,
visible=True
if line.name in ['average', 'all budgets'] else False)
])
# Add to legend
legend_it.append((name, renderers[-1]))
if name in ['average', 'all budgets'] or 'budget' in name:
# Fill area (uncertainty)
# Defined as sequence of coordinates, so for step-effect double and arange accordingly ([(t0, v0), (t1, v0), (t1, v1), ... (tn, vn-1)])
band_x = np.append(line.time, line.time[::-1])
band_y = np.append(line.lower, line.upper[::-1])
renderers[-1].extend([
p.patch(band_x,
band_y,
color='#7570B3',
fill_alpha=0.2,
visible=True if line.name
in ['average', 'all budgets'] else False)
])
# Tooltips
tooltips = [("estimated performance", "@mean"), ("at-time", "@time")]
p.add_tools(
HoverTool(
renderers=[i for s in renderers for i in s],
tooltips=tooltips,
))
# MAKE hovertips stay fixed in position
# callback=CustomJS(code="""
# var tooltips = document.getElementsByClassName("bk-tooltip");
# for (var i = 0, len = tooltips.length; i < len; i ++) {
# tooltips[i].style.top = ""; // unset what bokeh.js sets
# tooltips[i].style.left = "";
# tooltips[i].style.bottom = "0px";
# tooltips[i].style.left = "0px";
# }
# """)))
# TODO optional: activate different tooltips for different renderers, doesn't work properly
#tooltips_configs = tooltips[:] + [(p, '@'+p) for p in hp_names]
#if 'average' in [l.name for l in lines]:
# p.add_tools(HoverTool(renderers=[renderers[0]], tooltips=tooltips_avg ))#, mode='vline'))
# Wrap renderers in nested lists for checkbox-code
checkbox, select_all, select_none = get_checkbox(
renderers, [l[0] for l in legend_it])
checkbox.active = [0]
# Tilt tick labels and configure axis labels
p.xaxis.major_label_orientation = 3 / 4
p.xaxis.axis_label_text_font_size = p.yaxis.axis_label_text_font_size = "15pt"
p.xaxis.major_label_text_font_size = p.yaxis.major_label_text_font_size = "12pt"
p.title.text_font_size = "15pt"
legend = Legend(
items=legend_it,
location='bottom_left', #(0, -60),
label_text_font_size="8pt")
legend.click_policy = "hide"
p.add_layout(legend, 'right')
# Assign objects and save png's
layout = row(
p,
column(
widgetbox(checkbox, width=100),
row(widgetbox(select_all, width=50),
widgetbox(select_none, width=50))))
output_path = os.path.join(self.output_dir, output_fn)
export_bokeh(p, output_path, self.logger)
self.plots.append(output_path)
return layout
def plot(self,
X,
conf_list: list,
runs_per_quantile,
inc_list: list = None,
contour_data=None,
time_slider=False):
"""
plots sampled configuration in 2d-space;
uses bokeh for interactive plot
saves results in self.output, if set
Parameters
----------
X: np.array
np.array with 2-d coordinates for each configuration
conf_list: list
list of ALL configurations in the same order as X
runs_per_quantile: list[np.array]
configurator-run to be analyzed, as a np.array with
the number of target-algorithm-runs per config per quantile.
inc_list: list
list of incumbents (Configuration)
contour_data: list
contour data (xx,yy,Z)
time_slider: bool
whether or not to have a time_slider-widget on cfp-plot
INCREASES FILE-SIZE DRAMATICALLY
Returns
-------
(script, div): str
script and div of the bokeh-figure
over_time_paths: List[str]
list with paths to the different quantiled timesteps of the
configurator run (for static evaluation)
"""
if not inc_list:
inc_list = []
over_time_paths = [] # development of the search space over time
hp_names = [
k.name for k in # Hyperparameter names
conf_list[0].configuration_space.get_hyperparameters()
]
# Get individual sources for quantiles
sources = [
self._plot_get_source(conf_list, quantiled_run, X, inc_list,
hp_names)
for quantiled_run in runs_per_quantile
]
# Define what appears in tooltips
# TODO add only important parameters (needs to change order of exec pimp before conf-footprints)
hover = HoverTool(
tooltips=[('type', '@type'), ('origin',
'@origin'), ('runs', '@runs')] +
[(k, '@' + escape_parameter_name(k)) for k in hp_names])
# bokeh-figure
x_range = [min(X[:, 0]) - 1, max(X[:, 0]) + 1]
y_range = [min(X[:, 1]) - 1, max(X[:, 1]) + 1]
scatter_glyph_render_groups = []
for idx, source in enumerate(sources):
if not time_slider or idx == 0:
# Only plot all quantiles in one plot if timeslider is on
p = figure(plot_height=500,
plot_width=600,
tools=[hover, 'save'],
x_range=x_range,
y_range=y_range)
if contour_data is not None:
p = self._plot_contour(p, contour_data, x_range, y_range)
views, markers = self._plot_create_views(source)
self.logger.debug("Plotting quantile %d!", idx)
scatter_glyph_render_groups.append(
self._plot_scatter(p, source, views, markers))
if self.output_dir:
file_path = "cfp_over_time/configurator_footprint" + str(
idx) + ".png"
over_time_paths.append(os.path.join(self.output_dir,
file_path))
self.logger.debug("Saving plot to %s", over_time_paths[-1])
export_bokeh(p, over_time_paths[-1], self.logger)
if time_slider:
self.logger.debug("Adding timeslider")
slider = self._plot_get_timeslider(scatter_glyph_render_groups)
layout = column(p, widgetbox(slider))
else:
self.logger.debug("Not adding timeslider")
layout = column(p)
script, div = components(layout)
if self.output_dir:
path = os.path.join(self.output_dir,
"content/images/configurator_footprint.png")
export_bokeh(p, path, self.logger)
return (script, div), over_time_paths
def plot(self,
X,
conf_list: list,
runs_per_quantile,
inc_list: list=None,
contour_data=None,
use_timeslider=False,
use_checkbox=True,
timeslider_labels=None):
"""
plots sampled configuration in 2d-space;
uses bokeh for interactive plot
saves results in self.output, if set
Parameters
----------
X: np.array
np.array with 2-d coordinates for each configuration
conf_list: list
list of ALL configurations in the same order as X
runs_per_quantile: list[np.array]
configurator-run to be analyzed, as a np.array with
the number of target-algorithm-runs per config per quantile.
inc_list: list
list of incumbents (Configuration)
contour_data: list
contour data (xx,yy,Z)
use_timeslider: bool
whether or not to have a time_slider-widget on cfp-plot
INCREASES FILE-SIZE DRAMATICALLY
use_checkbox: bool
have checkboxes to toggle individual runs
Returns
-------
(script, div): str
script and div of the bokeh-figure
over_time_paths: List[str]
list with paths to the different quantiled timesteps of the
configurator run (for static evaluation)
"""
if not inc_list:
inc_list = []
over_time_paths = [] # development of the search space over time
hp_names = [k.name for k in # Hyperparameter names
conf_list[0].configuration_space.get_hyperparameters()]
# bokeh-figure
x_range = [min(X[:, 0]) - 1, max(X[:, 0]) + 1]
y_range = [min(X[:, 1]) - 1, max(X[:, 1]) + 1]
# Get individual sources for quantiles
sources, used_configs = zip(*[self._plot_get_source(conf_list, quantiled_run, X, inc_list, hp_names)
for quantiled_run in runs_per_quantile])
# We collect all glyphs in one list
# Then we have to dicts to identify groups of glyphs (for interactivity)
# They map the name of the group to a list of indices (of the respective glyphs that are in the group)
# Those indices refer to the main list of all glyphs
# This is necessary to enable interactivity for two inputs at the same time
all_glyphs = []
overtime_groups = {}
run_groups = {run : [] for run in self.configs_in_run.keys()}
# Iterate over quantiles (this updates overtime_groups)
for idx, source, u_cfgs in zip(range(len(sources)), sources, used_configs):
# Create new plot if necessary (only plot all quantiles in one single plot if timeslider is on)
if not use_timeslider or idx == 0:
p = self._create_figure(x_range, y_range)
if contour_data is not None: # TODO
contour_handles, color_mapper = self._plot_contour(p, contour_data, x_range, y_range)
# Create views and scatter
views, views_by_run, markers = self._create_views(source, u_cfgs)
scatter_handles = self._scatter(p, source, views, markers)
self.logger.debug("Quantile %d: %d scatter-handles", idx, len(scatter_handles))
if len(scatter_handles) == 0:
self.logger.debug("No configs in quantile %d (?!)", idx)
continue
# Add to groups
start = len(all_glyphs)
all_glyphs.extend(scatter_handles)
overtime_groups[str(idx)] = [str(i) for i in range(start, len(all_glyphs))]
for run, indices in views_by_run.items():
run_groups[run].extend([str(start + i) for i in indices])
# Write to file
if self.output_dir:
file_path = "cfp_over_time/configurator_footprint" + str(idx) + ".png"
over_time_paths.append(os.path.join(self.output_dir, file_path))
self.logger.debug("Saving plot to %s", over_time_paths[-1])
export_bokeh(p, over_time_paths[-1], self.logger)
# Add hovertool (define what appears in tooltips)
# TODO add only important parameters (needs to change order of exec pimp before conf-footprints)
hover = HoverTool(tooltips=[('type', '@type'), ('origin', '@origin'), ('runs', '@runs')] +
[(k, '@' + escape_parameter_name(k)) for k in hp_names],
renderers=all_glyphs)
p.add_tools(hover)
# Build dashboard
timeslider, checkbox, select_all, select_none, checkbox_title = self._get_widgets(all_glyphs, overtime_groups, run_groups,
slider_labels=timeslider_labels)
contour_checkbox, contour_title = self._contour_radiobuttongroup(contour_handles, color_mapper)
layout = p
if use_timeslider:
self.logger.debug("Adding timeslider")
layout = column(layout, widgetbox(timeslider))
if use_checkbox:
self.logger.debug("Adding checkboxes")
layout = row(layout,
column(widgetbox(checkbox_title),
widgetbox(checkbox),
row(widgetbox(select_all, width=100),
widgetbox(select_none, width=100)),
widgetbox(contour_title),
widgetbox(contour_checkbox)))
if self.output_dir:
path = os.path.join(self.output_dir, "content/images/configurator_footprint.png")
export_bokeh(p, path, self.logger)
return layout, over_time_paths
def plot_interactive_footprint(self):
"""Use bokeh to create an interactive algorithm footprint with zoom and
hover tooltips. Should avoid problems with overplotting (since we can
zoom) and provide better information about instances."""
features = np.array(self.features_2d)
instances = self.insts
runhistory = self.rh
algo = {v: k for k, v in self.algo_name.items()}
incumbent = algo['incumbent']
default = algo['default']
source = ColumnDataSource(data=dict(x=features[:, 0], y=features[:,
1]))
# Add all necessary information for incumbent and default
source.add(instances, 'instance_name')
instance_set = [
'train' if i in self.train_feats.keys() else 'test'
for i in instances
]
source.add(instance_set, 'instance_set') # train or test
for config, name in [(incumbent, 'incumbent'), (default, 'default')]:
cost = get_cost_dict_for_config(runhistory, config)
source.add([cost[i] for i in instances], '{}_cost'.format(name))
# TODO should be in function
good, bad = self._get_good_bad(config)
color = [
1 if idx in good else 0 for idx, i in enumerate(instances)
]
# TODO end
color = ['blue' if c else 'red' for c in color]
self.logger.debug("%s colors: %s", name, str(color))
source.add(color, '{}_color'.format(name))
source.add(source.data['default_color'], 'color')
# Define what appears in tooltips
hover = HoverTool(tooltips=[
('instance name', '@instance_name'),
('def cost', '@default_cost'),
('inc_cost', '@incumbent_cost'),
('set', '@instance_set'),
])
# Add radio-button
def_inc_callback = CustomJS(args=dict(source=source),
code="""
var data = source.data;
if (cb_obj.active == 0) {
data['color'] = data['default_color'];
} else {
data['color'] = data['incumbent_color'];
}
source.change.emit();
""")
def_inc_radio_button = RadioButtonGroup(
labels=["default", "incumbent"],
active=0,
callback=def_inc_callback)
# Plot
x_range = DataRange1d(bounds='auto',
start=min(features[:, 0]) - 1,
end=max(features[:, 0]) + 1)
y_range = DataRange1d(bounds='auto',
start=min(features[:, 1]) - 1,
end=max(features[:, 1]) + 1)
p = figure(
plot_height=500,
plot_width=600,
tools=[hover, 'save', 'wheel_zoom', 'box_zoom', 'pan', 'reset'],
active_drag='box_zoom',
x_range=x_range,
y_range=y_range)
# Scatter train and test individually to toggle them
train_view = CDSView(
source=source,
filters=[GroupFilter(column_name='instance_set', group='train')])
test_view = CDSView(
source=source,
filters=[GroupFilter(column_name='instance_set', group='test')])
train = p.scatter(x='x',
y='y',
source=source,
view=train_view,
color='color')
test = p.scatter(x='x',
y='y',
source=source,
view=test_view,
color='color')
p.xaxis.axis_label, p.yaxis.axis_label = 'principal component 1', 'principal component 2'
p.xaxis.axis_label_text_font_size = p.yaxis.axis_label_text_font_size = "15pt"
train_test_callback = CustomJS(args=dict(source=source,
train_view=train,
test_view=test),
code="""
var data = source.data;
if (cb_obj.active == 0) {
train_view.visible = true;
test_view.visible = true;
} else if (cb_obj.active == 1) {
train_view.visible = true;
test_view.visible = false;
} else {
train_view.visible = false;
test_view.visible = true;
}
""")
train_test_radio_button = RadioButtonGroup(
labels=["all", "train", "test"],
active=0,
callback=train_test_callback)
# Export and return
if self.output_dir:
path = os.path.join(self.output_dir,
"content/images/algorithm_footprint.png")
export_bokeh(p, path, self.logger)
layout = column(
p,
row(widgetbox(def_inc_radio_button),
widgetbox(train_test_radio_button)))
return layout
def plot(self,
rh: RunHistory,
runs: List[ConfiguratorRun],
output_fn: str = "performance_over_time.png",
validator: Union[None, Validator] = None):
""" Plot performance over time, using all trajectory entries
with max_time = wallclock_limit or (if inf) the highest
recorded time
Parameters
----------
rh: RunHistory
runhistory to use
runs: List[SMACrun]
list of configurator-runs
output_fn: str
path to output-png
validator: TODO description
"""
self.logger.debug(
"Estimating costs over time for %d runs, save png in %s.",
len(runs), output_fn)
validated = True # TODO ?
if len(runs) > 1:
# If there is more than one run, we average over the runs
means, times = [], []
all_times = []
for run in runs:
# Ignore variances as we plot variance over runs
validated = validated and run.traj
mean, _, time = self._get_mean_var_time(
validator, run.traj, not run.validated_runhistory, rh)
means.append(mean.flatten())
all_times.extend(time)
times.append(time)
means = np.array(means)
times = np.array(times)
all_times = np.array(sorted(all_times))
at = [0 for _ in runs
] # keep track at which timestep each trajectory is
m = [np.nan
for _ in runs] # used to compute the mean over the timesteps
mean = np.ones((len(all_times), 1)) * -1
var = np.ones((len(all_times), 1)) * -1
upper = np.ones((len(all_times), 1)) * -1
lower = np.ones((len(all_times), 1)) * -1
for time_idx, t in enumerate(all_times):
for traj_idx, entry_idx in enumerate(at):
try:
if t == times[traj_idx][entry_idx]:
m[traj_idx] = means[traj_idx][entry_idx]
at[traj_idx] += 1
except IndexError:
pass # Reached the end of one trajectory. No need to check it further
# var[time_idx][0] = np.nanvar(m)
u, l, m_ = np.nanpercentile(m, 75), np.nanpercentile(
m, 25), np.nanpercentile(m, 50)
# self.logger.debug((mean[time_idx][0] + np.sqrt(var[time_idx][0]), mean[time_idx][0],
# mean[time_idx][0] - np.sqrt(var[time_idx][0])))
# self.logger.debug((l, m_, u))
upper[time_idx][0] = u
mean[time_idx][0] = m_
lower[time_idx][0] = l
time = all_times
else: # no new statistics computation necessary
validated = True if runs[0].validated_runhistory else False
mean, var, time = self._get_mean_var_time(validator, runs[0].traj,
not validated, rh)
upper = lower = mean
mean = mean[:, 0]
upper = upper[:, 0]
lower = lower[:, 0]
uncertainty_upper = upper # mean + np.sqrt(var)
uncertainty_lower = lower # mean - np.sqrt(var)
clip_y_lower = False
if self.scenario.run_obj == 'runtime': # y-axis on log -> clip plot
# Determine clipping point from lowest legal value
clip_y_lower = min(
list(uncertainty_lower[uncertainty_lower > 0]) +
list(mean)) * 0.8
uncertainty_lower[uncertainty_lower <= 0] = clip_y_lower * 0.9
time_double = [t for sub in zip(time, time) for t in sub][1:-1]
mean_double = [t for sub in zip(mean, mean) for t in sub][:-2]
source = ColumnDataSource(
data=dict(x=time_double, y=mean_double, epm_perf=mean_double))
hover = HoverTool(tooltips=[("performance",
"@epm_perf"), ("at-time", "@x")])
p = figure(plot_width=700,
plot_height=500,
tools=[hover, 'save'],
x_range=Range1d(max(min(time), 1), max(time)),
x_axis_type='log',
y_axis_type='log'
if self.scenario.run_obj == 'runtime' else 'linear',
title="Cost over time")
if clip_y_lower:
p.y_range = Range1d(clip_y_lower, 1.2 * max(uncertainty_upper))
# start after 1% of the configuration budget
# p.x_range = Range1d(min(time) + (max(time) - min(time)) * 0.01, max(time))
# Plot
label = self.scenario.run_obj
label = '{}{}'.format('validated ' if validated else 'estimated ',
label)
p.line('x', 'y', source=source, legend=label)
# Fill area (uncertainty)
# Defined as sequence of coordinates, so for step-effect double and
# arange accordingly ([(t0, v0), (t1, v0), (t1, v1), ... (tn, vn-1)])
time_double = [t for sub in zip(time, time) for t in sub][1:-1]
uncertainty_lower_double = [
u for sub in zip(uncertainty_lower, uncertainty_lower) for u in sub
][:-2]
uncertainty_upper_double = [
u for sub in zip(uncertainty_upper, uncertainty_upper) for u in sub
][:-2]
band_x = np.append(time_double, time_double[::-1])
band_y = np.append(uncertainty_lower_double,
uncertainty_upper_double[::-1])
p.patch(band_x, band_y, color='#7570B3', fill_alpha=0.2)
# Tilt tick labels
p.xaxis.major_label_orientation = 3 / 4
p.legend.location = "top_right"
p.xaxis.axis_label = "time (sec)"
p.yaxis.axis_label = label
p.xaxis.axis_label_text_font_size = "15pt"
p.yaxis.axis_label_text_font_size = "15pt"
p.xaxis.major_label_text_font_size = "12pt"
p.yaxis.major_label_text_font_size = "12pt"
p.title.text_font_size = "15pt"
p.legend.label_text_font_size = "15pt"
script, div = components(p)
export_bokeh(p, os.path.join(self.output_dir, output_fn), self.logger)
return script, div
