How to use pypesto - 10 common examples

def gaussian_problem():
    def nllh(x):
        return - gaussian_llh(x)

    objective = pypesto.Objective(fun=nllh)
    problem = pypesto.Problem(objective=objective, lb=[-10], ub=[10])
    return problem

def create_problem():
    objective = rosen_for_sensi(2)['obj']
    lb = [-3, -3, -3, -3, -3]
    ub = [3, 3, 3, 3, 3]
    x_fixed_indices = [1, 3]
    x_fixed_vals = [1, 1]
    problem = pypesto.Problem(objective=objective,
                              lb=lb, ub=ub,
                              x_fixed_indices=x_fixed_indices,
                              x_fixed_vals=x_fixed_vals)

    return problem

def create_problem():
    # define a pypesto objective
    objective = pypesto.Objective(fun=so.rosen,
                                  grad=so.rosen_der,
                                  hess=so.rosen_hess)

    # define a pypesto problem
    (lb, ub) = create_bounds()
    problem = pypesto.Problem(objective=objective, lb=lb, ub=ub)

    return problem

def create_optimization_results(objective):
    # create optimizer, pypesto problem and options
    options = {
        'maxiter': 200
    }
    optimizer = optimize.ScipyOptimizer(method='TNC', options=options)

    lb = -2 * np.ones(2)
    ub = 2 * np.ones(2)
    problem = pypesto.Problem(objective, lb, ub)

    optimize_options = optimize.OptimizeOptions(allow_failed_starts=True)

    # run optimization
    result = optimize.minimize(
        problem=problem,
        optimizer=optimizer,
        n_starts=5,
        startpoint_method=pypesto.startpoint.uniform,
        options=optimize_options
    )

    return problem, result, optimizer

def test_prior():
    """Check that priors are defined for sampling."""
    # define negative log posterior
    posterior_fun = pypesto.Objective(fun=negative_log_posterior)

    # define negative log prior
    prior_fun = pypesto.Objective(fun=negative_log_prior)

    # define pypesto prior object
    prior_object = pypesto.NegLogPriors(objectives=[prior_fun])

    # define pypesto problem using prior object
    test_problem = pypesto.Problem(objective=posterior_fun,
                                   x_priors_defs=prior_object,
                                   lb=-10, ub=10,
                                   x_names=['x'])

    sampler = sample.AdaptiveMetropolisSampler()

    result = sample.sample(test_problem, n_samples=1e4, sampler=sampler,
                           x0=np.array([0.]))

    # get log prior values of first chain
    logprior_trace = -result.sample_result.trace_neglogprior[0, :]

    # check that not all entries are zero
    assert (logprior_trace != 0.).any()

    # get samples of first chain

filename = ".test_logging.tmp"
    pypesto.logging.log_to_file(logging.DEBUG, filename)
    logger = logging.getLogger('pypesto')
    if os.path.exists(filename):
        os.remove(filename)
    fh = logging.FileHandler(filename)
    fh.setLevel(logging.DEBUG)
    logger.addHandler(fh)
    logger.info("start test")

    # problem definition
    def fun(_):
        raise Exception("This function cannot be called.")

    objective = pypesto.Objective(fun=fun)
    problem = pypesto.Problem(objective, -1, 1)

    optimizer = pypesto.optimize.ScipyOptimizer()
    options = {'allow_failed_starts': True}

    # optimization
    pypesto.optimize.minimize(problem, optimizer, 5, options=options)

    # assert logging worked
    assert os.path.exists(filename)
    f = open(filename, 'rb')
    content = str(f.read())
    f.close()

    # tidy up
    os.remove(filename)

def _test_basic(engine):
    # set up problem
    objective = test_objective.rosen_for_sensi(max_sensi_order=2)['obj']
    lb = 0 * np.ones((1, 2))
    ub = 1 * np.ones((1, 2))
    problem = pypesto.Problem(objective, lb, ub)
    optimizer = pypesto.optimize.ScipyOptimizer(options={'maxiter': 10})
    result = pypesto.optimize.minimize(
        problem=problem, n_starts=5, engine=engine, optimizer=optimizer)
    assert len(result.optimize_result.as_list()) == 5

def test_profile_with_fixed_parameters():
    """Test using profiles with fixed parameters."""
    obj = test_objective.rosen_for_sensi(max_sensi_order=1)['obj']

    lb = -2 * np.ones(5)
    ub = 2 * np.ones(5)
    problem = pypesto.Problem(
        objective=obj, lb=lb, ub=ub,
        x_fixed_vals=[0.5, -1.8], x_fixed_indices=[0, 3])

    optimizer = optimize.ScipyOptimizer(options={'maxiter': 50})
    result = optimize.minimize(
        problem=problem, optimizer=optimizer, n_starts=2)

    for i_method, next_guess_method in enumerate([
            'fixed_step', 'adaptive_step_order_0',
            'adaptive_step_order_1', 'adaptive_step_regression']):
        print(next_guess_method)
        profile.parameter_profile(
            problem=problem, result=result, optimizer=optimizer,
            next_guess_method=next_guess_method)

        # standard plotting
        axes = visualize.profiles(result, profile_list_ids=i_method)
        assert len(axes) == 3
        visualize.profile_cis(result, profile_list=i_method)

fh.setLevel(logging.DEBUG)
    logger.addHandler(fh)
    logger.info("start test")

    # problem definition
    def fun(_):
        raise Exception("This function cannot be called.")

    objective = pypesto.Objective(fun=fun)
    problem = pypesto.Problem(objective, -1, 1)

    optimizer = pypesto.optimize.ScipyOptimizer()
    options = {'allow_failed_starts': True}

    # optimization
    pypesto.optimize.minimize(problem, optimizer, 5, options=options)

    # assert logging worked
    assert os.path.exists(filename)
    f = open(filename, 'rb')
    content = str(f.read())
    f.close()

    # tidy up
    os.remove(filename)

    # check if error message got inserted
    assert "fail" in content

def create_optimization_history():
    # create the pypesto problem
    problem = create_problem()

    # create optimizer
    optimizer_options = {'maxiter': 200}
    optimizer = optimize.ScipyOptimizer(
        method='TNC', options=optimizer_options)

    history_options = pypesto.HistoryOptions(
        trace_record=True, trace_save_iter=1)

    # run optimization
    optimize_options = optimize.OptimizeOptions(allow_failed_starts=True)
    result_with_trace = optimize.minimize(
        problem=problem,
        optimizer=optimizer,
        n_starts=5,
        startpoint_method=pypesto.startpoint.uniform,
        options=optimize_options,
        history_options=history_options
    )

    return result_with_trace