Python skopt.gp_minimize方法代码示例

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def run_measure(initial_point_generator, n_initial_points=10):
    start = time.time()
    # n_repeats must set to a much higher value to obtain meaningful results.
    n_repeats = 1
    res = run(gp_minimize, initial_point_generator,
              n_initial_points=n_initial_points, n_repeats=n_repeats)
    duration = time.time() - start
    # print("%s %s: %.2f s" % (initial_point_generator,
    #                          str(init_point_gen_kwargs),
    #                          duration))
    return res
#############################################################################
# Objective
# =========
#
# The objective of this example is to find one of these minima in as
# few iterations as possible. One iteration is defined as one call
# to the :class:`benchmarks.hart6` function.
#
# We will evaluate each model several times using a different seed for the
# random number generator. Then compare the average performance of these
# models. This makes the comparison more robust against models that get
# "lucky". 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_mixed_categoricals(initgen):

    space = Space([
        Categorical(name="x", categories=["1", "2", "3"]),
        Categorical(name="y", categories=[4, 5, 6]),
        Real(name="z", low=1.0, high=5.0)
    ])

    def objective(param_list):
        x = param_list[0]
        y = param_list[1]
        z = param_list[2]
        loss = int(x) + y * z
        return loss

    res = gp_minimize(objective, space, n_calls=12, random_state=1,
                      initial_point_generator=initgen)
    assert res["x"] in [['1', 4, 1.0], ['2', 4, 1.0]] 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_evaluate_min_params():
    res = gp_minimize(bench3,
                      [(-2.0, 2.0)],
                      x0=[0.],
                      noise=1e-8,
                      n_calls=8,
                      n_random_starts=3,
                      random_state=1)

    x_min, f_min = expected_minimum(res, random_state=1)
    x_min2, f_min2 = expected_minimum_random_sampling(res,
                                                      n_random_starts=1000,
                                                      random_state=1)
    plots.plot_gaussian_process(res)
    assert _evaluate_min_params(res, params='result') == res.x
    assert _evaluate_min_params(res, params=[1.]) == [1.]
    assert _evaluate_min_params(res, params='expected_minimum',
                                random_state=1) == x_min
    assert _evaluate_min_params(res, params='expected_minimum',
                                n_minimum_search=20,
                                random_state=1) == x_min
    assert _evaluate_min_params(res, params='expected_minimum_random',
                                n_minimum_search=1000,
                                random_state=1) == x_min2 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def optimize(n_trials):
   """Run the gp_minimize function"""
   dimensions = [(4, 10),       # batch size
                 (1e-3, 1e-1),  # learning rate
                 (0.7, 1.5),     # std mult
                 (3, 6),        # filter_size
                 (2, 4),        # n_rings
                 (0.5,1.5)]       # phase_preconditioner

   x0 = [5, 1e-2, 1., 5, 2, 1.]

   print gp_minimize(wrapper_function, dimensions, x0=x0, n_calls=n_trials, verbose=True, callback=dump) 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def optimize_threshold(self, xtrain, ytrain, xval, yval):
        ytrain_pred = self.predict_labels(xtrain, raw_prob=True)
        yval_pred = self.predict_labels(xval, raw_prob=True)
        self.opt_threshold = 0.5
        ytrain_pred_labels = self.get_labels_from_prob(ytrain_pred, threshold=self.opt_threshold)
        yval_pred_labels = self.get_labels_from_prob(yval_pred, threshold=self.opt_threshold)
        train_f1_score = f1_score(ytrain_pred_labels, ytrain)
        val_f1_score = f1_score(yval_pred_labels, yval)
        print(f"train f1 score: {train_f1_score}, val f1 score: {val_f1_score}")

        f1_train_partial = partial(self.get_f1score_for_optimization, y_true=ytrain.copy(), y_pred=ytrain_pred.copy(), ismin=True)
        n_searches = 50
        dim_0 = Real(low=0.2, high=0.8, name='dim_0')
        dimensions = [dim_0]
        search_result = gp_minimize(func=f1_train_partial,
                                    dimensions=dimensions,
                                    acq_func='gp_hedge',  # Expected Improvement.
                                    n_calls=n_searches,
                                    # n_jobs=n_cpu,
                                    verbose=False)

        self.opt_threshold = search_result.x
        if isinstance(self.opt_threshold,list):
            self.opt_threshold = self.opt_threshold[0]
        self.optimum_threshold_filename = f"model_threshold_{'_'.join(str(v) for k, v in model_params.items())}.npy"
        np.save(os.path.join(f"{model_params['model_save_dir']}",self.optimum_threshold_filename), self.opt_threshold)
        train_f1_score = self.get_f1score_for_optimization(self.opt_threshold, y_true=ytrain, y_pred=ytrain_pred)
        val_f1_score = self.get_f1score_for_optimization(self.opt_threshold, y_true=yval, y_pred=yval_pred )
        print(f"optimized train f1 score: {train_f1_score}, optimized val f1 score: {val_f1_score}") 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def check_minimize(func, y_opt, bounds, acq_optimizer, acq_func,
                   margin, n_calls, n_initial_points=10, init_gen="random"):
    r = gp_minimize(func, bounds, acq_optimizer=acq_optimizer,
                    acq_func=acq_func, n_initial_points=n_initial_points,
                    n_calls=n_calls, random_state=1,
                    initial_point_generator=init_gen,
                    noise=1e-10)
    assert r.fun < y_opt + margin 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_n_jobs():
    r_single = gp_minimize(bench3, [(-2.0, 2.0)], acq_optimizer="lbfgs",
                           acq_func="EI", n_calls=2, n_initial_points=1,
                           random_state=1, noise=1e-10)
    r_double = gp_minimize(bench3, [(-2.0, 2.0)], acq_optimizer="lbfgs",
                           acq_func="EI", n_calls=2, n_initial_points=1,
                           random_state=1, noise=1e-10, n_jobs=2)
    assert_array_equal(r_single.x_iters, r_double.x_iters) 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_gpr_default():
    """Smoke test that gp_minimize does not fail for default values."""
    gp_minimize(branin, ((-5.0, 10.0), (0.0, 15.0)), n_initial_points=1,
                n_calls=2) 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_use_given_estimator():
    """ Test that gp_minimize does not use default estimator if one is passed
    in explicitly. """
    domain = [(1.0, 2.0), (3.0, 4.0)]
    noise_correct = 1e+5
    noise_fake = 1e-10
    estimator = cook_estimator("GP", domain, noise=noise_correct)
    res = gp_minimize(branin, domain, n_calls=1, n_initial_points=1,
                      base_estimator=estimator, noise=noise_fake)

    assert res['models'][-1].noise == noise_correct 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_use_given_estimator_with_max_model_size():
    """ Test that gp_minimize does not use default estimator if one is passed
    in explicitly. """
    domain = [(1.0, 2.0), (3.0, 4.0)]
    noise_correct = 1e+5
    noise_fake = 1e-10
    estimator = cook_estimator("GP", domain, noise=noise_correct)
    res = gp_minimize(branin, domain, n_calls=1, n_initial_points=1,
                      base_estimator=estimator, noise=noise_fake,
                      model_queue_size=1)
    assert len(res['models']) == 1
    assert res['models'][-1].noise == noise_correct 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_mixed_categoricals2(initgen):
    space = Space([
        Categorical(name="x", categories=["1", "2", "3"]),
        Categorical(name="y", categories=[4, 5, 6])
    ])

    def objective(param_list):
        x = param_list[0]
        y = param_list[1]
        loss = int(x) + y
        return loss

    res = gp_minimize(objective, space, n_calls=12, random_state=1,
                      initial_point_generator=initgen)
    assert res["x"] == ['1', 4] 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_dump_and_load():
    res = gp_minimize(bench3,
                      [(-2.0, 2.0)],
                      x0=[0.],
                      acq_func="LCB",
                      n_calls=2,
                      n_random_starts=0,
                      random_state=1)

    # Test normal dumping and loading
    with tempfile.TemporaryFile() as f:
        dump(res, f)
        f.seek(0)
        res_loaded = load(f)
    check_optimization_results_equality(res, res_loaded)
    assert "func" in res_loaded.specs["args"]

    # Test dumping without objective function
    with tempfile.TemporaryFile() as f:
        dump(res, f, store_objective=False)
        f.seek(0)
        res_loaded = load(f)
    check_optimization_results_equality(res, res_loaded)
    assert not ("func" in res_loaded.specs["args"])

    # Delete the objective function and dump the modified object
    del res.specs["args"]["func"]
    with tempfile.TemporaryFile() as f:
        dump(res, f, store_objective=False)
        f.seek(0)
        res_loaded = load(f)
    check_optimization_results_equality(res, res_loaded)
    assert not ("func" in res_loaded.specs["args"]) 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_expected_minimum():
    res = gp_minimize(bench3,
                      [(-2.0, 2.0)],
                      x0=[0.],
                      noise=1e-8,
                      n_calls=8,
                      n_random_starts=3,
                      random_state=1)

    x_min, f_min = expected_minimum(res, random_state=1)
    x_min2, f_min2 = expected_minimum(res, random_state=1)

    assert f_min <= res.fun  # true since noise ~= 0.0
    assert x_min == x_min2
    assert f_min == f_min2 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_expected_minimum_random_sampling():
    res = gp_minimize(bench3,
                      [(-2.0, 2.0)],
                      x0=[0.],
                      noise=1e-8,
                      n_calls=8,
                      n_random_starts=3,
                      random_state=1)

    x_min, f_min = expected_minimum_random_sampling(res, random_state=1)
    x_min2, f_min2 = expected_minimum_random_sampling(res, random_state=1)

    assert f_min <= res.fun  # true since noise ~= 0.0
    assert x_min == x_min2
    assert f_min == f_min2 

# 需要导入模块: import skopt [as 别名]
# 或者: from skopt import gp_minimize [as 别名]
def test_plots_work_without_cat():
    """Basic smoke tests to make sure plotting doesn't crash."""
    SPACE = [
        Integer(1, 20, name='max_depth'),
        Integer(2, 100, name='min_samples_split'),
        Integer(5, 30, name='min_samples_leaf'),
        Integer(1, 30, name='max_features'),
    ]

    def objective(params):
        clf = DecisionTreeClassifier(random_state=3,
                                     **{dim.name: val
                                        for dim, val in zip(SPACE, params)
                                        if dim.name != 'dummy'})
        return -np.mean(cross_val_score(clf, *load_breast_cancer(True)))

    res = gp_minimize(objective, SPACE, n_calls=10, random_state=3)
    plots.plot_convergence(res)
    plots.plot_evaluations(res)
    plots.plot_objective(res)
    plots.plot_objective(res,
                         minimum='expected_minimum')
    plots.plot_objective(res,
                         sample_source='expected_minimum',
                         n_minimum_search=10)
    plots.plot_objective(res, sample_source='result')
    plots.plot_regret(res)

    # TODO: Compare plots to known good results?
    # Look into how matplotlib does this.