def map_parameter_estimate(self):
res = self.pst.res
assert res is not None
# build the prior expectation parameter vector
prior_expt = self.pst.parameter_data.loc[:,["parval1"]].copy()
islog = self.pst.parameter_data.partrans == "log"
prior_expt.loc[islog] = prior_expt.loc[islog].apply(np.log10)
prior_expt = Matrix.from_dataframe(prior_expt)
prior_expt.col_names = ["prior_expt"]
# build the residual vector
res_vec = Matrix.from_dataframe(res.loc[:,["residual"]])
# form the terms of Schur's complement
b = self.parcov * self.jco.T
c = ((self.jco * self.parcov * self.jco.T) + self.obscov).inv
bc = Matrix((b * c).x, row_names=b.row_names, col_names=c.col_names)
# calc posterior expectation
upgrade = bc * res_vec
upgrade.col_names = ["prior_expt"]
post_expt = prior_expt + upgrade
# post processing - back log transform
post_expt = pd.DataFrame(data=post_expt.x,index=post_expt.row_names,
columns=["post_expt"])
post_expt.loc[:,"prior_expt"] = prior_expt.x.flatten()
post_expt.loc[islog,:] = 10.0**post_expt.loc[islog,:]
# unc_sum = self.get_parameter_summary()
# post_expt.loc[:,"standard_deviation"] = unc_sum.post_var.apply(np.sqrt)
post_expt.sort_index(inplace=True)
return post_expt
def map_parameter_estimate(self):
res = self.pst.res
assert res is not None
# build the prior expectation parameter vector
prior_expt = self.pst.parameter_data.loc[:,["parval1"]].copy()
islog = self.pst.parameter_data.partrans == "log"
prior_expt.loc[islog] = prior_expt.loc[islog].apply(np.log10)
prior_expt = Matrix.from_dataframe(prior_expt)
prior_expt.col_names = ["prior_expt"]
# build the residual vector
res_vec = Matrix.from_dataframe(res.loc[:,["residual"]])
# form the terms of Schur's complement
b = self.parcov * self.jco.T
c = ((self.jco * self.parcov * self.jco.T) + self.obscov).inv
bc = Matrix((b * c).x, row_names=b.row_names, col_names=c.col_names)
# calc posterior expectation
upgrade = bc * res_vec
upgrade.col_names = ["prior_expt"]
post_expt = prior_expt + upgrade
# post processing - back log transform
post_expt = pd.DataFrame(data=post_expt.x,index=post_expt.row_names,
columns=["post_expt"])
post_expt.loc[:,"prior_expt"] = prior_expt.x.flatten()
post_expt.loc[islog,:] = 10.0**post_expt.loc[islog,:]
# unc_sum = self.get_parameter_summary()
# post_expt.loc[:,"standard_deviation"] = unc_sum.post_var.apply(np.sqrt)
post_expt.sort_index(inplace=True)
return post_expt
def map_parameter_estimate(self):
res = self.pst.res
assert res is not None
# build the prior expectation parameter vector
prior_expt = self.pst.parameter_data.loc[:, ["parval1"]].copy()
islog = self.pst.parameter_data.partrans == "log"
prior_expt.loc[islog] = prior_expt.loc[islog].apply(np.log10)
prior_expt = Matrix.from_dataframe(prior_expt)
prior_expt.col_names = ["prior_expt"]
# build the residual vector
res_vec = Matrix.from_dataframe(res.loc[:, ["residual"]])
# form the terms of Schur's complement
b = self.parcov * self.jco.T
c = ((self.jco * self.parcov * self.jco.T) + self.obscov).inv
bc = Matrix((b * c).x, row_names=b.row_names, col_names=c.col_names)
# calc posterior expectation
term2 = bc * res_vec
term2.col_names = ["prior_expt"]
post_expt = prior_expt + term2
# post processing - back log transform
post_expt = pd.DataFrame(data=post_expt.x,
index=post_expt.row_names,
columns=["post_expt"])
post_expt.loc[islog, :] = 10.0**post_expt.loc[islog, :]
return post_expt
def map_forecast_estimate(self):
assert self.forecasts is not None
maps = {}
islog = self.pst.parameter_data.partrans == "log"
par_map = self.map_parameter_estimate
par_map.loc[islog, :] = np.log10(par_map.loc[islog, :])
par_map = Matrix.from_dataframe(par_map)
for forecast in self.forecasts:
fname = forecast.col_names[0]
pr = self.pst.res.loc[fname, "modelled"]
maps[forecast.col_names[0]] = pr + (forecast.T * par_map).x[0, 0]
return maps
def map_forecast_estimate(self):
assert self.forecasts is not None
islog = self.pst.parameter_data.partrans == "log"
par_map = self.map_parameter_estimate
par_map.loc[islog,:] = np.log10(par_map.loc[islog,:])
par_map = Matrix.from_dataframe(par_map)
posts,priors = [],[]
for forecast in self.forecasts:
fname = forecast.col_names[0]
pr = self.pst.res.loc[fname,"modelled"]
priors.append(pr)
posts.append(pr + (forecast.T * par_map).x[0, 0])
return pd.DataFrame(data=np.array([priors,posts]).transpose(),
columns=["prior_expt","post_expt"],
index=self.forecast_names)
def map_forecast_estimate(self):
assert self.forecasts is not None
islog = self.pst.parameter_data.partrans == "log"
par_map = self.map_parameter_estimate
par_map.loc[islog,:] = np.log10(par_map.loc[islog,:])
par_map = Matrix.from_dataframe(par_map.loc[:,["post_expt"]])
posts,priors = [],[]
post_expt = (self.predictions.T * par_map).to_dataframe()
for fname in self.forecast_names:
#fname = forecast.col_names[0]
pr = self.pst.res.loc[fname,"modelled"]
priors.append(pr)
posts.append(pr + post_expt.loc[fname,"post_expt"])
return pd.DataFrame(data=np.array([priors,posts]).transpose(),
columns=["prior_expt","post_expt"],
index=self.forecast_names)
def map_forecast_estimate(self):
""" get the prior and posterior forecast (prediction) expectations.
Returns
-------
pandas.DataFrame : pandas.DataFrame
dataframe with prior and posterior forecast expected values
"""
assert self.forecasts is not None
islog = self.pst.parameter_data.partrans == "log"
par_map = self.map_parameter_estimate
par_map.loc[islog,:] = np.log10(par_map.loc[islog,:])
par_map = Matrix.from_dataframe(par_map.loc[:,["post_expt"]])
posts,priors = [],[]
post_expt = (self.predictions.T * par_map).to_dataframe()
for fname in self.forecast_names:
#fname = forecast.col_names[0]
pr = self.pst.res.loc[fname,"modelled"]
priors.append(pr)
posts.append(pr + post_expt.loc[fname,"post_expt"])
return pd.DataFrame(data=np.array([priors,posts]).transpose(),
columns=["prior_expt","post_expt"],
index=self.forecast_names)