def test_adapt_single_model(population_strategy: PopulationStrategy):
n = 10
df = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
w = np.ones(n) / n
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
population_strategy.adapt_population_size([kernel], np.array([1.]))
assert population_strategy.nr_particles > 0
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))
def test_adapt_two_models(population_strategy: PopulationStrategy):
n = 10
kernels = []
for _ in range(2):
df = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
w = np.ones(n) / n
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
kernels.append(kernel)
population_strategy.adapt_population_size(kernels, np.array([.7, .2]))
assert population_strategy.nr_particles > 0
def test_no_parameters(population_strategy: PopulationStrategy):
n = 10
df = pd.DataFrame(index=list(range(n)))
w = np.ones(n) / n
kernels = []
for _ in range(2):
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
kernels.append(kernel)
population_strategy.adapt_population_size(kernels, np.array([.7, .3]))
assert population_strategy.nr_particles > 0
def test_no_parameters(population_strategy: PopulationStrategy):
n = 10
df = pd.DataFrame(index=list(range(n)))
w = np.ones(n) / n
kernels = []
for _ in range(2):
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
kernels.append(kernel)
population_strategy.update(kernels, np.array([0.7, 0.3]), t=0)
assert population_strategy(t=0) > 0
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 test_one_with_one_without_parameters(
population_strategy: PopulationStrategy):
n = 10
kernels = []
df_without = pd.DataFrame(index=list(range(n)))
w_without = np.ones(n) / n
kernel_without = MultivariateNormalTransition()
kernel_without.fit(df_without, w_without)
kernels.append(kernel_without)
df_with = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
w_with = np.ones(n) / n
kernel_with = MultivariateNormalTransition()
kernel_with.fit(df_with, w_with)
kernels.append(kernel_with)
population_strategy.adapt_population_size(kernels, np.array([.7, .3]))
assert population_strategy.nr_particles > 0
def test_transitions_not_modified(population_strategy: PopulationStrategy):
n = 10
kernels = []
test_points = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
for _ in range(2):
df = pd.DataFrame([{"s": np.random.rand()} for _ in range(n)])
w = np.ones(n) / n
kernel = MultivariateNormalTransition()
kernel.fit(df, w)
kernels.append(kernel)
test_weights = [k.pdf(test_points) for k in kernels]
population_strategy.adapt_population_size(kernels, np.array([.7, .2]))
after_adaptation_weights = [k.pdf(test_points) for k in kernels]
same = all([(k1 == k2).all()
for k1, k2 in zip(test_weights, after_adaptation_weights)])
err_msg = ("Population strategy {}"
" modified the transitions".format(population_strategy))
assert same, err_msg
df, w = history.get_distribution(m=1)
params = test_priors[1]
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)
