How to use the pyabc.ConstantPopulationSize function in pyabc

# We define two models, but they are identical so far
    models = [model, model]
    models = list(map(SimpleModel, models))


    # However, our models' priors are not the same. Their mean differs.
    mu_x_1, mu_x_2 = 0, 1
    parameter_given_model_prior_distribution = [
        Distribution(x=st.norm(mu_x_1, sigma)),
        Distribution(x=st.norm(mu_x_2, sigma))]

    # We plug all the ABC setup together
    nr_populations = 3
    population_size = ConstantPopulationSize(400)

    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 PercentileDistance(measures_to_use=["y"]), population_size,
                 eps=MedianEpsilon(.2),
                 transitions=[transition(), transition()],
                 sampler=sampler)

    # Finally we add meta data such as model names and define where to store the results
    # y_observed is the important piece here: our actual observation.
    y_observed = 1
    abc.new(db_path, {"y": y_observed})

    # We run the ABC with 3 populations max
    minimum_epsilon = .05
    history = abc.run(minimum_epsilon, max_nr_populations=nr_populations)

def test_gaussian_multiple_populations(db_path, sampler):
    sigma_x = 1
    sigma_y = .5
    y_observed = 2

    def model(args):
        return {"y": st.norm(args['x'], sigma_y).rvs()}

    models = [model]
    models = list(map(SimpleModel, models))
    nr_populations = 4
    population_size = ConstantPopulationSize(600)
    parameter_given_model_prior_distribution = [Distribution(x=st.norm(0, sigma_x))]
    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 MinMaxDistance(measures_to_use=["y"]),
                 population_size,
                 eps=MedianEpsilon(.2),
                 sampler=sampler)

    abc.new(db_path, {"y": y_observed})

    minimum_epsilon = -1

    abc.do_not_stop_when_only_single_model_alive()
    history = abc.run(minimum_epsilon, max_nr_populations=nr_populations)
    posterior_x, posterior_weight = history.get_distribution(0, None)
    posterior_x = posterior_x["x"].values
    sort_indices = np.argsort(posterior_x)

def test_gaussian_single_population(db_path, sampler):
    sigma_prior = 1
    sigma_ground_truth = 1
    observed_data = 1

    def model(args):
        return {"y": st.norm(args['x'], sigma_ground_truth).rvs()}

    models = [model]
    models = list(map(SimpleModel, models))
    nr_populations = 1
    population_size = ConstantPopulationSize(600)
    parameter_given_model_prior_distribution = [Distribution(x=RV("norm", 0,
                                                                  sigma_prior))
                                                ]
    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 MinMaxDistance(measures_to_use=["y"]),
                 population_size,
                 eps=MedianEpsilon(.1),
                 sampler=sampler)
    abc.new(db_path, {"y": observed_data})

    minimum_epsilon = -1


    abc.do_not_stop_when_only_single_model_alive()
    history = abc.run(minimum_epsilon, max_nr_populations=nr_populations)
    posterior_x, posterior_weight = history.get_distribution(0, None)

def test_all_in_one_model(db_path, sampler):
    models = [AllInOneModel() for _ in range(2)]
    population_size = ConstantPopulationSize(800)
    parameter_given_model_prior_distribution = [Distribution(theta=RV("beta",
                                                                      1, 1))
                                                for _ in range(2)]
    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 MinMaxDistance(measures_to_use=["result"]),
                 population_size,
                 eps=MedianEpsilon(.1),
                 sampler=sampler)
    abc.new(db_path, {"result": 2})

    minimum_epsilon = .2
    history = abc.run(minimum_epsilon, max_nr_populations=3)
    mp = history.get_model_probabilities(history.max_t)
    assert abs(mp.p[0] - .5) + abs(mp.p[1] - .5) < .08

def test_beta_binomial_different_priors_initial_epsilon_from_sample(db_path,
                                                                    sampler):
    binomial_n = 5

    def model(args):
        return {"result": st.binom(binomial_n, args.theta).rvs()}

    models = [model for _ in range(2)]
    models = list(map(SimpleModel, models))
    population_size = ConstantPopulationSize(800)
    a1, b1 = 1, 1
    a2, b2 = 10, 1
    parameter_given_model_prior_distribution = [Distribution(theta=RV("beta",
                                                                      a1, b1)),
                                                Distribution(theta=RV("beta",
                                                                      a2, b2))]
    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 MinMaxDistance(measures_to_use=["result"]),
                 population_size,
                 eps=MedianEpsilon(median_multiplier=.9),
                 sampler=sampler)
    n1 = 2
    abc.new(db_path, {"result": n1})

    minimum_epsilon = -1
    history = abc.run(minimum_epsilon, max_nr_populations=5)

def test_two_competing_gaussians_single_population(db_path, sampler, transition):
    sigma_x = .5
    sigma_y = .5
    y_observed = 1

    def model(args):
        return {"y": st.norm(args['x'], sigma_y).rvs()}

    models = [model, model]
    models = list(map(SimpleModel, models))
    population_size = ConstantPopulationSize(500)
    mu_x_1, mu_x_2 = 0, 1
    parameter_given_model_prior_distribution = [
        Distribution(x=st.norm(mu_x_1, sigma_x)),
        Distribution(x=st.norm(mu_x_2, sigma_x))]
    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 MinMaxDistance(measures_to_use=["y"]),
                 population_size,
                 transitions=[transition(), transition()],
                 eps=MedianEpsilon(.02),
                 sampler=sampler)
    abc.new(db_path, {"y": y_observed})

    minimum_epsilon = -1
    nr_populations = 1
    abc.do_not_stop_when_only_single_model_alive()
    history = abc.run(minimum_epsilon, max_nr_populations=1)

return {"y": st.norm(args['x'], sigma).rvs()}

    # We define two models, but they are identical so far
    models = [model, model]
    models = list(map(SimpleModel, models))

    # However, our models' priors are not the same. Their mean differs.
    mu_x_1, mu_x_2 = 0, 1
    parameter_given_model_prior_distribution = [
        Distribution(x=RV("norm", mu_x_1, sigma)),
        Distribution(x=RV("norm", mu_x_2, sigma))
    ]

    # We plug all the ABC setup together
    nr_populations = 2
    pop_size = ConstantPopulationSize(23, nr_samples_per_parameter=n_sim)
    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 PercentileDistance(measures_to_use=["y"]),
                 pop_size,
                 eps=MedianEpsilon(),
                 sampler=sampler)

    # Finally we add meta data such as model names and
    # define where to store the results
    # y_observed is the important piece here: our actual observation.
    y_observed = 1
    abc.new(db_path, {"y": y_observed})

    # We run the ABC with 3 populations max
    minimum_epsilon = .05
    history = abc.run(minimum_epsilon, max_nr_populations=nr_populations)

def test_gaussian_multiple_populations_crossval_kde(db_path, sampler):
    sigma_x = 1
    sigma_y = .5
    y_observed = 2

    def model(args):
        return {"y": st.norm(args['x'], sigma_y).rvs()}

    models = [model]
    models = list(map(SimpleModel, models))
    nr_populations = 4
    population_size = ConstantPopulationSize(600)
    parameter_given_model_prior_distribution = [Distribution(x=st.norm(0, sigma_x))]
    parameter_perturbation_kernels = [GridSearchCV(MultivariateNormalTransition(),
                                      {"scaling": np.logspace(-1, 1.5, 5)})]
    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 MinMaxDistance(measures_to_use=["y"]),
                 population_size,
                 transitions=parameter_perturbation_kernels,
                 eps=MedianEpsilon(.2),
                 sampler=sampler)
    abc.new(db_path, {"y": y_observed})

    minimum_epsilon = -1

    abc.do_not_stop_when_only_single_model_alive()
    history = abc.run(minimum_epsilon, max_nr_populations=nr_populations)
    posterior_x, posterior_weight = history.get_distribution(0, None)

return {"y": st.norm(args['x'], sigma).rvs()}

    # We define two models, but they are identical so far
    models = [model, model]
    models = list(map(SimpleModel, models))

    # However, our models' priors are not the same. Their mean differs.
    mu_x_1, mu_x_2 = 0, 1
    parameter_given_model_prior_distribution = [
        Distribution(x=st.norm(mu_x_1, sigma)),
        Distribution(x=st.norm(mu_x_2, sigma))
    ]

    # We plug all the ABC setup together
    nr_populations = 1
    population_size = ConstantPopulationSize(20)
    abc = ABCSMC(models, parameter_given_model_prior_distribution,
                 PercentileDistance(measures_to_use=["y"]),
                 population_size,
                 eps=MedianEpsilon(.2),
                 sampler=sampler)

    # Finally we add meta data such as model names and
    # define where to store the results
    # y_observed is the important piece here: our actual observation.
    y_observed = 2
    abc.new(db_path, {"y": y_observed})

    # We run the ABC with 3 populations max
    minimum_epsilon = .05
    history = abc.run(minimum_epsilon, max_nr_populations=nr_populations)