def plot_features(ro: StepSequence, policy: Policy):
"""
Plot all features given the policy and the observation trajectories.
:param policy: linear policy used during the rollout
:param ro: input rollout
"""
if not isinstance(policy, LinearPolicy):
print_cbt(
'Plotting of the feature values is only supports linear policies!',
'y')
return
if hasattr(ro, 'observations'):
# Use recorded time stamps if possible
t = ro.env_infos.get('t', np.arange(0, ro.length)) if hasattr(
ro, 'env_infos') else np.arange(0, ro.length)
# Recover the features from the observations
feat_vals = policy.eval_feats(to.from_numpy(ro.observations))
dim_feat = range(feat_vals.shape[1])
if len(dim_feat) <= 6:
divisor = 2
elif len(dim_feat) <= 12:
divisor = 4
else:
divisor = 8
num_cols = int(np.ceil(len(dim_feat) / divisor))
num_rows = int(np.ceil(len(dim_feat) / num_cols))
fig, axs = plt.subplots(num_rows,
num_cols,
figsize=(num_cols * 5, num_rows * 3),
constrained_layout=True)
fig.suptitle('Feature values over Time')
plt.subplots_adjust(hspace=.5)
colors = plt.get_cmap('tab20')(np.linspace(0, 1, len(dim_feat)))
if len(dim_feat) == 1:
axs.plot(t,
feat_vals[:-1, dim_feat[0]],
label=_get_obs_label(ro, dim_feat[0]))
axs.legend()
else:
for i in range(num_rows):
for j in range(num_cols):
if j + i * num_cols < len(dim_feat):
# Omit the last observation for simplicity
axs[i, j].plot(t,
feat_vals[:-1, j + i * num_cols],
label=rf'$\phi_{j + i*num_cols}$',
c=colors[j + i * num_cols])
axs[i, j].legend()
else:
# We might create more subplots than there are observations
pass
plt.show()
def plot_features(ro: StepSequence, policy: Policy):
"""
Plot all features given the policy and the observation trajectories.
:param policy: linear policy used during the rollout
:param ro: input rollout
"""
if not isinstance(policy, LinearPolicy):
print_cbt(
"Plotting of the feature values is only supports linear policies!",
"r")
return
if hasattr(ro, "observations"):
# Use recorded time stamps if possible
t = getattr(ro, "time", np.arange(0, ro.length + 1))[:-1]
# Recover the features from the observations
feat_vals = policy.eval_feats(to.from_numpy(ro.observations))
dim_feat = range(feat_vals.shape[1])
if len(dim_feat) <= 6:
divisor = 2
elif len(dim_feat) <= 12:
divisor = 4
else:
divisor = 8
num_cols = int(np.ceil(len(dim_feat) / divisor))
num_rows = int(np.ceil(len(dim_feat) / num_cols))
fig, axs = plt.subplots(num_rows,
num_cols,
figsize=(num_cols * 5, num_rows * 3),
tight_layout=True)
axs = np.atleast_2d(axs)
axs = correct_atleast_2d(axs)
fig.canvas.manager.set_window_title("Feature Values over Time")
plt.subplots_adjust(hspace=0.5)
colors = plt.get_cmap("tab20")(np.linspace(0, 1, len(dim_feat)))
if len(dim_feat) == 1:
axs[0, 0].plot(t,
feat_vals[:-1, dim_feat[0]],
label=_get_obs_label(ro, dim_feat[0]))
axs[0, 0].legend()
else:
for i in range(num_rows):
for j in range(num_cols):
if j + i * num_cols < len(dim_feat):
# Omit the last observation for simplicity
axs[i, j].plot(t,
feat_vals[:-1, j + i * num_cols],
c=colors[j + i * num_cols])
axs[i, j].set_ylabel(rf"$\phi_{{{j + i*num_cols}}}$")
else:
# We might create more subplots than there are observations
axs[i, j].remove()