class KernelSampler(object):
def __init__(self, outcome_upper_support, outcome_lower_support,
outcome_names, treatment_names, backdoor_variables, data,
dep_type, indep_type, bw, defaults):
self._data = data
self._outcome_names = outcome_names
self._treatment_names = treatment_names
self._backdoor_variables = backdoor_variables
self.dep_type = dep_type
self.indep_type = indep_type
self.bw = bw
self.defaults = defaults
self.outcome_lower_support = outcome_lower_support
self.outcome_upper_support = outcome_upper_support
self.conditional_density = KDEMultivariateConditional(
endog=self._data[self._outcome_names],
exog=self._data[self._treatment_names + self._backdoor_variables],
dep_type=''.join(self.dep_type),
indep_type=''.join(self.indep_type),
bw=self.bw,
defaults=self.defaults)
def sample_point(self, x_z):
y_bw = 1.06 * self._data[self._outcome_names].std() * (
self._data[self._outcome_names].count())**(-1. / 5.)
n = 5 * np.ceil(
(self.outcome_upper_support - self.outcome_lower_support) / y_bw)
cum_ranges = [
np.linspace(self.outcome_lower_support[i],
self.outcome_upper_support[i], n[i])
for i in range(len(self._outcome_names))
]
res = np.meshgrid(*cum_ranges)
points = np.array(res).reshape(len(self._outcome_names),
np.int(n.cumprod()[-1])).T
x_z_repeated = np.repeat(x_z,
len(points)).reshape(len(points), len(x_z))
cdf_vals = self._evaluate_cdf(points, x_z_repeated)
cdf_vals = np.hstack([[0.], cdf_vals, [1.]])
points = np.vstack([[self.outcome_lower_support - 3. * y_bw], points,
[self.outcome_upper_support + 3. * y_bw]])
inv_cdf = interp1d(cdf_vals.flatten(),
points.flatten(),
fill_value=0.,
axis=0)
r = np.random.rand()
try:
return inv_cdf(r)
except ValueError:
return self.sample_point(x_z)
def _evaluate_cdf(self, y, x_z):
return self.conditional_density.cdf(endog_predict=[y],
exog_predict=x_z)
