def draw_vert_avg(ax, statistics):
print('draw_vert_avg')
ymax = ax.get_ylim()[1]
a_hits_df = stat_tools.find_first_hits(statistics, acc.acc_requirement)
a_mean = a_hits_df['end_time'].mean()
a_line = ax.vlines(a_mean,
ymin=0,
ymax=ymax,
colors=c.bg_dot_color,
linewidth=3,
path_effects=[c.white_outline])
_set_zorder(a_line)
best_params = _find_best_params(statistics)
best_stat = statistics.select_by_values(best_params)
b_hits_df = stat_tools.find_first_hits(best_stat, acc.acc_requirement)
b_mean = b_hits_df['end_time'].mean()
b_color = next(c.iter_fg_dot_color())
b_line = ax.vlines(b_mean,
ymin=0,
ymax=ymax,
colors=b_color,
linewidth=3,
path_effects=[c.white_outline])
_set_zorder(b_line)
_add_2_important([(a_mean, 0), (b_mean, 0)])
def _draw_hits(ax, stat, color=None):
print('\t_draw_hits')
hits_df = stat_tools.find_first_hits(stat, acc.acc_requirement)
xs = hits_df['end_time']
ys = hits_df['val_acc']
path = ax.scatter(xs, ys, c=color, edgecolor='white', s=72)
_add_2_important(list(zip(xs, ys)))
_set_zorder(path)
def draw_box_plot(ax, statistics, by_param):
df = stat_tools.find_first_hits(statistics, acc.acc_requirement)
df = df.dropna(axis='index')
colors = c.iter_fg_dot_color()
data = []
param_vals = []
for param_val, local_df in df.groupby(by_param).__iter__():
data.append(local_df['end_time'])
param_vals.append(param_val)
box_plot = ax.boxplot(data, vert=False, labels=param_vals,
notch=False, patch_artist=True)
for box, flier, color in zip(box_plot['boxes'], box_plot['fliers'], colors):
box.set_facecolor(color)
flier.set_markerfacecolor(color)
ax.set(xlabel='end_time(s)', ylabel=by_param)
def collect_hits(config):
hostname_all = []
hit_times_all = []
hit_accs_all = []
for host in config['hosts']:
print(f'processing host="{host}"')
statistics = stat_tools.Statistics(**config['hosts'][host])
hit_df = stat_tools.find_first_hits(statistics,
acc.acc_requirement,
reverse=acc.reverse_acc_req)
hit_times = hit_df['end_time'].values
hit_accs = hit_df['val_acc'].values
hostname_all.append(host)
hit_times_all.append(hit_times)
hit_accs_all.append(hit_accs)
return hostname_all, hit_times_all, hit_accs_all
def draw_fig(statistics, by_param, facecolor=c.white):
hit_df = stat_tools.find_first_hits(statistics, acc.acc_requirement)
num_groups = hit_df.loc[:, by_param].nunique()
fig = plt.figure(figsize=[10, 4 * num_groups], constrained_layout=True)
grid = gridspec.GridSpec(num_groups, 1, figure=fig)
grid.update(wspace=0.025, hspace=0.01)
axs = [fig.add_subplot(spec) for spec in grid]
draw_group(axs, draw_hist, hit_df, by_param, label=True)
draw_group(axs, draw_norm_dist, hit_df, by_param)
fig.set_facecolor(facecolor)
for ax in axs:
ax.set_facecolor(facecolor)
ax.set_ylabel('probabilistic distribution(%)')
# only on last(bottom) plot
ax.set_xlabel('end_time(%)')
return fig
def draw_fig(statistics, by_param, facecolor=c.white):
fig, ax = plt.subplots()
hit_df = stat_tools.find_first_hits(statistics, acc.acc_requirement)
draw_group(ax,
draw_cdf,
hit_df,
by_param,
label=True,
n_bins=n_bins,
z_offset=10)
draw_group(ax, draw_theo_cdf, hit_df, by_param, on=False)
fig.set_facecolor(facecolor)
ax.set_facecolor(facecolor)
ax.set(xlabel="end_time(s)", ylabel="Cumulative Distribution(%)")
ax.legend(title=by_param, loc='upper left')
fig.tight_layout()
return fig
def main():
options = parse_argv()
statistics = stat_tools.Statistics(path=options.path,
params=options.params)
base_dir = os.path.dirname(options.path)
first_hit_df = stat_tools.find_first_hits(statistics, acc.acc_requirement)
for param in statistics.params:
print(f'param = {param}')
fig = draw_fig(first_hit_df, param=param, facecolor=options.facecolor)
# showing figure in window
if not options.quiet:
plt.show()
# save image to the same directory as statistics.csv
image_path = os.path.join(base_dir, f'first_hit_avg_bar_{param}_.png')
fig.savefig(image_path)
# close current figure before drawing again
plt.close(fig)