class RVkde(RVBase):
def __init__(self, df, w, key, min, max):
self.df = df
self.w = w
self.key = key
self.kde = MultivariateNormalTransition(scaling=1)
self.kde.fit(df[[key]], w)
self.min = min
self.max = max
# Calucating truncated away cdf to compensate in pdf
min_xs = np.linspace(self.min - 10., self.min, num=200)
min_pdfs = [
self.kde.pdf(pd.DataFrame({self.key: [x]})) for x in min_xs
]
min_cdf = np.sum(min_pdfs) * 10 / 200
max_xs = np.linspace(self.max, self.max + 10, num=200)
max_pdfs = [
self.kde.pdf(pd.DataFrame({self.key: [x]})) for x in max_xs
]
max_cdf = np.sum(max_pdfs) * 10 / 200
self.trunc_cdf = min_cdf + max_cdf
def rvs(self):
x = self.kde.rvs()
while x[0] < self.min or x[0] > self.max:
x = self.kde.rvs()
return (x[0])
def pdf(self, x):
p = 0.
if x > self.min and x < self.max:
x = pd.DataFrame({self.key: [x]})
p = self.kde.pdf(x)
return p / (1 - self.trunc_cdf)
def copy(self):
return copy.deepcopy(self)
def pmf(self):
return 0.
def cdf(self, x):
cdf = 0
if x > self.min:
xs = np.linspace(self.min, x, num=100)
pdfs = [self.pdf(x) for x in xs]
cdf = np.sum(pdfs) * (x - self.min) / 100
return cdf
def priors_from_kde(df,w):
prior_dict = {}
for key in df.columns:
kde = MultivariateNormalTransition(scaling=1)
kde.fit(df[[key]], w)
x = kde.rvs(1000)
α,β,loc,scale = scst.beta.fit(x[key])
prior_dict.update({key: RV("beta", α,β,loc,scale)})
return(Distribution(**prior_dict))
priors[1].decorator_repr()
pyabc.parameters.ParameterStructure
pyabc.parameters.from_dictionary_of_dictionaries()
#%%
from pyabc.visualization import kde_1d
from pyabc.transition import MultivariateNormalTransition
import pyabc.random_variables as rv
kde = MultivariateNormalTransition(scaling=1)
kde.fit(df[["p"]], w)
scst.beta.fit(kde.rvs(100), loc=0)
x, pdf = kde_1d(df, w, "p")
class PriorRV(rv.RVBase):
def __init__
priors[1]["p"]
sckde = scst.gaussian_kde(df["p"])
sckde.pdf(0.9)
beta_priors = [Distribution(**{key: RV("beta", 0.4, 0.4, loc=a, scale=(b - a)) for key, (a,b) in sample_param_spaces[mod].items()}) for mod in model_names]
