def plot_single_coefficient(self,
beta,
ylim=None,
ax=None,
highlight_median="green"):
"""
Plot the dynamic distribution of a single model coefficient.
Args:
beta: The coefficient to be plotted. A matrix. Rows are Monte Carlo
draws, and columns are time points.
ylim: A pair of numbers giving the lower and upper limits of the Y
axis. If 'None' then 'ylim' will be inferred from the range of the
data.
ax: A plt.Axes object on which to draw. If None then a new
plt.Figure and Axes will be created and drawn on function exit.
highlight_median: The name of a color used to draw the meadian of the
curves at each time point. The empty string signals not to add the
extra highlighting.
Returns:
The axes object containing the plot.
"""
fig = None
if ax is None:
fig, ax = plt.subplots(1, 1)
R.plot_dynamic_distribution(beta,
timestamps=self._unique_timestamps,
ax=ax,
ylim=ylim,
highlight_median=highlight_median)
if fig is not None:
fig.show()
return ax
def plot_state(self,
burn=None,
time=None,
show_actuals=True,
style=None,
scale=None,
ylim=None,
ax=None,
**kwargs):
if style is None:
style = "dynamic"
style = R.unique_match(style, ["dynamic", "boxplot"])
if scale is None:
scale = "linear"
scale = R.unique_match(scale, ["linear", "mean"])
niter = self._niter
if burn is None:
burn = self.suggest_burn()
if time is None:
time = self.original_series.index
state_contribution = np.zeros((niter, len(time)))
for model in self._state_models:
state_contribution += model.state_contribution
R.plot_dynamic_distribution(
curves=state_contribution,
timestamps=time,
ax=ax,
ylim=ylim,
**kwargs)
def plot_size(self, ax=None, burn: int = None, **kwargs):
fig = None
if ax is None:
fig, ax = plt.subplots(1, 1)
size = np.sum(self._beta_draws != 0, axis=1)
R.plot_dynamic_distribution(
size,
timestamps=self._unique_timestamps,
ax=ax,
xlab="Time",
ylab="Number Included Predictors",
)
if fig is not None:
fig.show()
return ax
def plot_state_contribution_default(self,
fig,
gridspec,
time,
burn=None,
ylim=None,
**kwargs):
"""
A default implementation of plot_state_distribution.
Plots the contribution of this state model to the conditional mean at
time t on the supplied set of axes using a dynamic distribution plot.
Args:
ax: The axes object on which to draw the plot.
time: The time axis.
burn: The number of iterations to discard as burn-in.
ylim: A pair giving the lower and upper limits on the y axis.
kwargs: Extra arguments passed to plot_dynamic_distribution.
Effects:
Creates a dynamic distribution plot on the supplied set of axes.
Returns:
The axes object.
"""
if burn > 0:
curves = self._state_contribution[burn:, :]
else:
curves = self._state_contribution
ax = fig.add_subplot(gridspec)
R.plot_dynamic_distribution(curves=curves,
timestamps=time,
ax=ax,
ylim=ylim,
**kwargs)
return ax
