def test_eight_schools_sample():
"""Compile a simple model."""
posterior = pystan.compile(program_code, data=schools_data)
fit = posterior.sample(num_chains=1, num_samples=200, num_warmup=200)
num_flat_params = schools_data['J'] * 2 + 2
assert fit.values.shape == (1, 200, num_flat_params)
df = fit.to_frame()
assert 'eta.1' in df.columns
assert len(df['eta.1']) == 200
def test_normal_sample_chains():
"""Sample from normal distribution with more than one chain."""
program_code = 'parameters {real y;} model {y ~ normal(0,1);}'
posterior = pystan.compile(program_code, data={})
assert posterior is not None
fit = posterior.sample(num_chains=2)
assert fit.values.shape == (2, 1000, 1) # 1 chain, n samples, 1 param
df = fit.to_frame()
assert len(df['y']) == 2000
assert -5 < df['y'].mean() < 5
def test_normal_sample_args():
"""Sample from normal distribution with compile arguments."""
program_code = 'parameters {real y;} model {y ~ normal(0,1);}'
posterior = pystan.compile(program_code, data={}, random_seed=1)
assert posterior is not None
fit = posterior.sample(num_samples=350, num_thin=2)
df = fit.to_frame()
assert len(df['y']) == 350 // 2
assert -5 < df['y'].mean() < 5
assert fit.to_panel().shape == (1, 350 // 2, 1) # 1 chain, n samples, 1 parameter
assert fit.to_panel().loc[:, :, 'y'].shape == (350 // 2, 1)
def test_vector_params():
"""Sample from a program with vector params."""
program_code = """
parameters {
vector[3] beta;
}
model {
beta ~ normal(0, 1);
}
"""
posterior = pystan.compile(program_code, data={})
fit = posterior.sample()
df = fit.to_frame()
assert all(df.columns == ['beta.1', 'beta.2', 'beta.3'])
assert len(df['beta.1']) > 100
def test_matrix_params_compile():
"""Sample from a program with matrix-valued params."""
program_code = """
data {
int<lower=2> K;
int<lower=1> D;
}
parameters {
matrix[K,D] beta;
}
model {
for (k in 1:K)
for (d in 1:D)
beta[k,d] ~ normal(0, 1);
}
"""
data = {'K': 9, 'D': 5}
posterior = pystan.compile(program_code, data=data)
assert posterior is not None
def test_matrix_params_sample():
"""Sample from a program with matrix-valued params."""
program_code = """
data {
int<lower=2> K;
int<lower=1> D;
}
parameters {
matrix[K,D] beta;
}
model {
for (k in 1:K)
for (d in 1:D)
beta[k,d] ~ normal(0, 1);
}
"""
data = {'K': 9, 'D': 5}
posterior = pystan.compile(program_code, data=data)
fit = posterior.sample()
df = fit.to_frame()
assert len(df.columns) == data['K'] * data['D']
assert len(df['beta.1.1']) > 100
def test_eight_schools_compile():
"""Compile a simple model."""
posterior = pystan.compile(program_code, data=schools_data)
assert posterior is not None
def test_normal_compile():
"""Compile a simple model."""
program_code = 'parameters {real y;} model {y ~ normal(0,1);}'
posterior = pystan.compile(program_code, data={})
assert posterior is not None