def get_sample(self, number_samples, input_values={}): #TODO: code duplication here
reformatted_input_values = reformat_sampler_input(pandas_frame2dict(input_values),
number_samples=number_samples)
raw_sample = self._get_sample(number_samples, observed=False, input_values=reformatted_input_values,
differentiable=False)
sample = reformat_sample_to_pandas(raw_sample)
return sample
def get_posterior_sample(self, number_samples, input_values={}):
reformatted_input_values = reformat_sampler_input(
pandas_frame2dict(input_values), number_samples=number_samples)
raw_sample = self._get_posterior_sample(
number_samples, input_values=reformatted_input_values)
sample = reformat_sample_to_pandas(raw_sample)
return sample
def get_timeseries_sample(self,
number_samples,
input_values={},
mode="prior"):
if mode == "prior":
sampler = self._get_sample
elif mode == "posterior":
sampler = self._get_posterior_sample
else:
raise ValueError(
"the mode of the sampler should be either the 'prior' or 'posterior' string"
)
reformatted_input_values = reformat_sampler_input(
pandas_frame2dict(input_values), number_samples=number_samples)
raw_sample = sampler(number_samples,
input_values=reformatted_input_values)
temporal_raw_sample = {
var: (self._get_time_stamp(var), value)
for var, value in raw_sample.items()
if var in self.temporal_variables
}
temporal_sample = reformat_temporal_sample_to_pandas_timeseries(
temporal_raw_sample)
return temporal_sample
def get_sample(self, number_samples, input_values={}):
reformatted_input_values = reformat_sampler_input(pandas_frame2dict(input_values),
number_samples=number_samples)
raw_sample = {self: self._get_sample(number_samples, resample=False,
observed=self.is_observed,
differentiable=False,
input_values=reformatted_input_values)[self]}
sample = reformat_sample_to_pandas(raw_sample)
self.reset()
return sample
def get_entropy(self, input_values={}):
reformatted_input_values = reformat_sampler_input(
pandas_frame2dict(input_values), number_samples=1)
raw_ent = self._get_entropy(reformatted_input_values)
ent = reformat_sample_to_pandas(raw_ent, number_samples=1)
return ent
def get_variance(self, input_values={}):
reformatted_input_values = reformat_sampler_input(
pandas_frame2dict(input_values), number_samples=1)
raw_variance = self._get_variance(reformatted_input_values)
variance = reformat_sample_to_pandas(raw_variance, number_samples=1)
return variance
def get_mean(self, input_values={}):
reformatted_input_values = reformat_sampler_input(
pandas_frame2dict(input_values), number_samples=1)
raw_mean = self._get_mean(reformatted_input_values)
mean = reformat_sample_to_pandas(raw_mean, number_samples=1)
return mean
