def plot_scaling_log(plt_df: pd.DataFrame,
sweep_vars: Optional[Sequence[str]] = None,
with_baseline=True) -> gg.ggplot:
"""Plot scaling of learning time against exponential baseline."""
p = _base_scaling(plt_df, sweep_vars, with_baseline)
p += gg.scale_x_log10(breaks=[5, 10, 20, 50])
p += gg.scale_y_log10(breaks=[100, 300, 1000, 3000, 10000, 30000])
p += gg.xlab('deep sea problem size (log scale)')
p += gg.ylab('#episodes until < 90% bad episodes (log scale)')
return plotting.facet_sweep_plot(p, sweep_vars)
def plot_scaling(plt_df: pd.DataFrame,
sweep_vars: Sequence[Text] = None,
with_baseline: bool = True) -> gg.ggplot:
"""Plot scaling of learning time against exponential baseline."""
p = _base_scaling(plt_df, sweep_vars, with_baseline)
p += gg.xlab('deep sea problem size')
p += gg.ylab('#episodes until < 90% bad episodes')
if with_baseline:
max_steps = np.minimum(NUM_EPISODES, plt_df.episode.max())
p += gg.coord_cartesian(ylim=(0, max_steps))
return plotting.facet_sweep_plot(p, sweep_vars)
def plot_regret(df_in: pd.DataFrame,
sweep_vars: Sequence[Text] = None) -> gg.ggplot:
"""Plot average regret of deep_sea through time by size."""
df = df_in.copy()
df = df[df['size'].isin([10, 20, 30, 40, 50])]
df['avg_bad'] = df.total_bad_episodes / df.episode
df['size'] = df['size'].astype('category')
p = (
gg.ggplot(df[df.episode <= NUM_EPISODES]) +
gg.aes('episode', 'avg_bad', group='size', colour='size') +
gg.geom_line(size=2, alpha=0.75) + gg.geom_hline(
gg.aes(yintercept=0.99), linetype='dashed', alpha=0.4, size=1.75) +
gg.geom_hline(gg.aes(yintercept=0.0), alpha=0) # axis hack
+ gg.ylab('average bad episodes') +
gg.scale_colour_manual(values=plotting.FIVE_COLOURS))
return plotting.facet_sweep_plot(p, sweep_vars)
def plot_scale(df: pd.DataFrame,
sweep_vars: Sequence[str] = None) -> gg.ggplot:
"""Plots the best episode observed by height_threshold."""
df = cp_swingup_preprocess(df_in=df)
group_vars = ['height_threshold']
if sweep_vars:
group_vars += sweep_vars
plt_df = df.groupby(group_vars)['best_episode'].max().reset_index()
p = (
gg.ggplot(plt_df) +
gg.aes(x='factor(height_threshold)',
y='best_episode',
colour='best_episode > {}'.format(GOOD_EPISODE)) +
gg.geom_point(size=5, alpha=0.8) +
gg.scale_colour_manual(values=['#d73027', '#313695']) +
gg.geom_hline(gg.aes(yintercept=0.0), alpha=0) # axis hack
+ gg.scale_x_discrete(breaks=[0, 0.25, 0.5, 0.75, 1.0]) +
gg.ylab('best return in first {} episodes'.format(NUM_EPISODES)) +
gg.xlab('height threshold'))
return plotting.facet_sweep_plot(p, sweep_vars)
