class DLMMixture(object):
"""
Mixture of DLMs (Class I type model)
Parameters
----------
models : dict
Notes
-----
cf. W&H Section 12.2
"""
def __init__(self, models):
self.models = models
self.names = sorted(models.keys())
mod = self.models.values()[0]
self.pred_like = DataMatrix(dict(
(k, v.pred_like) for k, v in models.iteritems()),
index=mod.dates)
@property
def post_model_prob(self):
cumprod = self.pred_like.cumprod()
return cumprod / cumprod.sum(1)
def plot_post_prob(self):
ratio = self.post_model_prob
ratio.plot(subplots=True, sharey=True)
ax = plt.gca()
ax.set_ylim([0, 1])
def get_weights(self, t):
weights = self.post_model_prob
return weights.xs(weights.index[t])
def plot_mu_density(self, t, index=0, support_thresh=0.1):
"""
Plot posterior densities for single model parameter over the set of
mixture components
Parameters
----------
t : int
time index, relative to response variable
index : int
parameter index to plot
Notes
-----
cf. West & Harrison Figure 12.3. Automatically annotating individual
component curves would probably be difficult.
"""
ix = index
dists = {}
for name in self.names:
model = self.models[name]
df = model.df[t]
mode = model.mu_mode[t + 1, ix]
scale = np.sqrt(model.mu_scale[t + 1, ix, ix])
dists[name] = stats.t(df, loc=mode, scale=scale)
plot_mixture(dists, self.get_weights(t), support_thresh=support_thresh)
def plot_forc_density(self, t, support_thresh=0.1):
"""
Plot posterior densities for 1-step forecasts
Parameters
----------
t : int
time index, relative to response variable
Notes
-----
cf. West & Harrison Figure 12.4.
"""
dists = {}
for name in self.names:
model = self.models[name]
df = model.df[t]
mode = model.forecast[t]
scale = np.sqrt(model.forc_var[t])
dists[name] = stats.t(df, loc=mode, scale=scale)
plot_mixture(dists, self.get_weights(t), support_thresh=support_thresh)
class DLMMixture(object):
"""
Mixture of DLMs (Class I type model)
Parameters
----------
models : dict
Notes
-----
cf. W&H Section 12.2
"""
def __init__(self, models):
self.models = models
self.names = sorted(models.keys())
mod = self.models.values()[0]
self.pred_like = DataMatrix(dict((k, v.pred_like)
for k, v in models.iteritems()),
index=mod.dates)
@property
def post_model_prob(self):
cumprod = self.pred_like.cumprod()
return cumprod / cumprod.sum(1)
def plot_post_prob(self):
ratio = self.post_model_prob
ratio.plot(subplots=True, sharey=True)
ax = plt.gca()
ax.set_ylim([0, 1])
def get_weights(self, t):
weights = self.post_model_prob
return weights.xs(weights.index[t])
def plot_mu_density(self, t, index=0, support_thresh=0.1):
"""
Plot posterior densities for single model parameter over the set of
mixture components
Parameters
----------
t : int
time index, relative to response variable
index : int
parameter index to plot
Notes
-----
cf. West & Harrison Figure 12.3. Automatically annotating individual
component curves would probably be difficult.
"""
ix = index
dists = {}
for name in self.names:
model = self.models[name]
df = model.df[t]
mode = model.mu_mode[t + 1, ix]
scale = np.sqrt(model.mu_scale[t + 1, ix, ix])
dists[name] = stats.t(df, loc=mode, scale=scale)
plot_mixture(dists, self.get_weights(t),
support_thresh=support_thresh)
def plot_forc_density(self, t, support_thresh=0.1):
"""
Plot posterior densities for 1-step forecasts
Parameters
----------
t : int
time index, relative to response variable
Notes
-----
cf. West & Harrison Figure 12.4.
"""
dists = {}
for name in self.names:
model = self.models[name]
df = model.df[t]
mode = model.forecast[t]
scale = np.sqrt(model.forc_var[t])
dists[name] = stats.t(df, loc=mode, scale=scale)
plot_mixture(dists, self.get_weights(t),
support_thresh=support_thresh)
