Python space.Integer方法代碼示例

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def bayes_search(self, **kwargs):
        """
        Grid search using skopt.BayesSearchCV

        Any parameters typically associated with BayesSearchCV (see
        Scikit-Optimize documentation) can be passed as keyword arguments to
        this function.

         The final dictionary used for the grid search is saved to
        `self.bayes_search_params`. This is updated with any parameters that
        are passed.

        Examples
        --------
        # Passing kwargs.
        self.bayes_search(search_spaces={'max_depth':Integer(4, 6)}, refit=True)
        """
        self.bayes_search_params.update(kwargs)
        self.bayes_search_ = BayesSearchCV(**self.bayes_search_params)
        self.bayes_search_.fit(self.x_train, self.transformed_y_train)
        return self.bayes_search_ 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_dimension_name():
    notnames = [1, 1., True]
    for n in notnames:
        with pytest.raises(ValueError) as exc:
            real = Real(1, 2, name=n)
            assert("Dimension's name must be either string or"
                   "None." == exc.value.args[0])
    s = Space([Real(1, 2, name="a"),
               Integer(1, 100, name="b"),
               Categorical(["red, blue"], name="c")])
    assert s["a"] == (0, s.dimensions[0])
    assert s["a", "c"] == [(0, s.dimensions[0]), (2, s.dimensions[2])]
    assert s[["a", "c"]] == [(0, s.dimensions[0]), (2, s.dimensions[2])]
    assert s[("a", "c")] == [(0, s.dimensions[0]), (2, s.dimensions[2])]
    assert s[0] == (0, s.dimensions[0])
    assert s[0, "c"] == [(0, s.dimensions[0]), (2, s.dimensions[2])]
    assert s[0, 2] == [(0, s.dimensions[0]), (2, s.dimensions[2])] 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def _knob_to_dimension(knob):
    if isinstance(knob, CategoricalKnob):
        return Categorical([x.value for x in knob.values])
    elif isinstance(knob, IntegerKnob):
        return Integer(knob.value_min, knob.value_max)
    elif isinstance(knob, FloatKnob):
        if knob.is_exp:
            # Avoid error in skopt when low/high are 0
            value_min = knob.value_min if knob.value_min != 0 else 1e-12
            value_max = knob.value_max if knob.value_max != 0 else 1e-12
            return Real(value_min, value_max, 'log-uniform')
        else:
            return Real(knob.value_min, knob.value_max, 'uniform')
    else:
        raise UnsupportedKnobError(knob.__class__) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def _fit_svc(n_jobs=1, n_points=1, cv=None):
    """
    Utility function to fit a larger classification task with SVC
    """

    X, y = make_classification(n_samples=1000, n_features=20, n_redundant=0,
                               n_informative=18, random_state=1,
                               n_clusters_per_class=1)

    opt = BayesSearchCV(
        SVC(),
        {
            'C': Real(1e-3, 1e+3, prior='log-uniform'),
            'gamma': Real(1e-3, 1e+1, prior='log-uniform'),
            'degree': Integer(1, 3),
        },
        n_jobs=n_jobs, n_iter=11, n_points=n_points, cv=cv,
        random_state=42,
    )

    opt.fit(X, y)
    assert opt.score(X, y) > 0.9

    opt2 = BayesSearchCV(
        SVC(),
        {
            'C': Real(1e-3, 1e+3, prior='log-uniform'),
            'gamma': Real(1e-3, 1e+1, prior='log-uniform'),
            'degree': Integer(1, 3),
        },
        n_jobs=n_jobs, n_iter=11, n_points=n_points, cv=cv,
        random_state=42,
    )

    opt2.fit(X, y)

    assert opt.score(X, y) == opt2.score(X, y) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_searchcv_rank():
    """
    Test whether results of BayesSearchCV can be reproduced with a fixed
    random state.
    """

    X, y = load_iris(True)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, train_size=0.75, random_state=0
    )

    random_state = 42

    opt = BayesSearchCV(
        SVC(random_state=random_state),
        {
            'C': Real(1e-6, 1e+6, prior='log-uniform'),
            'gamma': Real(1e-6, 1e+1, prior='log-uniform'),
            'degree': Integer(1, 8),
            'kernel': Categorical(['linear', 'poly', 'rbf']),
        },
        n_iter=11, random_state=random_state, return_train_score=True
    )

    opt.fit(X_train, y_train)
    results = opt.cv_results_

    test_rank = np.asarray(rankdata(-np.array(results["mean_test_score"]),
                                    method='min'), dtype=np.int32)
    train_rank = np.asarray(rankdata(-np.array(results["mean_train_score"]),
                                     method='min'), dtype=np.int32)

    assert_array_equal(np.array(results['rank_test_score']), test_rank)
    assert_array_equal(np.array(results['rank_train_score']), train_rank) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_dimensions():
    check_dimension(Real, (1., 4.), 2.251066014107722)
    check_dimension(Real, (1, 4), 2.251066014107722)
    check_dimension(Integer, (1, 4), 2)
    check_dimension(Integer, (1., 4.), 2)
    check_dimension(Integer, (1, 4), 2)
    check_categorical(("a", "b", "c", "d"), "b")
    check_categorical((1., 2., 3., 4.), 2.)
    check_categorical((1, 2, 3, 4), 2) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_integer():
    a = Integer(1, 10)
    for i in range(50):
        r = a.rvs(random_state=i)
        assert 1 <= r
        assert 11 >= r
        assert r in a

    random_values = a.rvs(random_state=0, n_samples=10)
    assert_array_equal(random_values.shape, (10))
    assert_array_equal(a.transform(random_values), random_values)
    assert_array_equal(a.inverse_transform(random_values), random_values) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_normalize_types():
    # can you pass a Space instance to the Space constructor?
    space = Space([(0.0, 1.0), Integer(-5, 5, dtype=int), (True, False)])
    space.set_transformer("normalize")
    X = [[0., -5, False]]
    Xt = np.zeros((1, 3))
    assert_array_equal(space.transform(X), Xt)
    assert_array_equal(space.inverse_transform(Xt), X)
    assert_array_equal(space.inverse_transform(space.transform(X)), X)
    assert isinstance(space.inverse_transform(Xt)[0][0], float)
    assert isinstance(space.inverse_transform(Xt)[0][1], int)
    assert isinstance(space.inverse_transform(Xt)[0][2], (np.bool_, bool)) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_valid_transformation():
    check_valid_transformation(Integer)
    check_valid_transformation(Real) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_integer_distance():
    ints = Integer(1, 10)
    for i in range(1, 10+1):
        assert_equal(ints.distance(4, i), abs(4 - i)) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_integer_distance_out_of_range():
    ints = Integer(1, 10)
    assert_raises_regex(RuntimeError, "compute distance for values within",
                        ints.distance, 11, 10) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_space_from_yaml():
    with NamedTemporaryFile(delete=False) as tmp:
        tmp.write(b"""
        Space:
            - Real:
                low: 0.0
                high: 1.0
            - Integer:
                low: -5
                high: 5
            - Categorical:
                categories:
                - a
                - b
                - c
            - Real:
                low: 1.0
                high: 5.0
                prior: log-uniform
            - Categorical:
                categories:
                - e
                - f
        """)
        tmp.flush()

        space = Space([(0.0, 1.0),
                       (-5, 5),
                       ("a", "b", "c"),
                       (1.0, 5.0, "log-uniform"),
                       ("e", "f")])

        space2 = Space.from_yaml(tmp.name)
        assert_equal(space, space2)
        tmp.close()
        os.unlink(tmp.name) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_space_names_in_use_named_args():
    space = [Integer(250, 2000, name='n_estimators')]

    @use_named_args(space)
    def objective(n_estimators):
        return n_estimators

    res = gp_minimize(objective, space, n_calls=10, random_state=0)
    best_params = dict(zip((s.name for s in res.space), res.x))
    assert 'n_estimators' in best_params
    assert res.space.dimensions[0].name == 'n_estimators' 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def __init__(self, configspace, **kwargs):
        super().__init__(
            configspace, reward_attribute=kwargs.get('reward_attribute'))
        self.hp_ordering = configspace.get_hyperparameter_names() # fix order of hyperparams in configspace.
        skopt_hpspace = []
        for hp in self.hp_ordering:
            hp_obj = configspace.get_hyperparameter(hp)
            hp_type = str(type(hp_obj)).lower() # type of hyperparam
            if 'integer' in hp_type:
                hp_dimension = Integer(low=int(hp_obj.lower), high=int(hp_obj.upper),name=hp)
            elif 'float' in hp_type:
                if hp_obj.log: # log10-scale hyperparmeter
                    hp_dimension = Real(low=float(hp_obj.lower), high=float(hp_obj.upper), prior='log-uniform', name=hp)
                else:
                    hp_dimension = Real(low=float(hp_obj.lower), high=float(hp_obj.upper), name=hp)
            elif 'categorical' in hp_type:
                hp_dimension = Categorical(hp_obj.choices, name=hp)
            elif 'ordinal' in hp_type:
                hp_dimension = Categorical(hp_obj.sequence, name=hp)
            else:
                raise ValueError("unknown hyperparameter type: %s" % hp)
            skopt_hpspace.append(hp_dimension)
        skopt_keys = {
            'base_estimator', 'n_random_starts', 'n_initial_points',
            'acq_func', 'acq_optimizer', 'random_state',  'model_queue_size',
            'acq_func_kwargs', 'acq_optimizer_kwargs'}
        skopt_kwargs = self._filter_skopt_kwargs(kwargs, skopt_keys)
        self.bayes_optimizer = Optimizer(
            dimensions=skopt_hpspace, **skopt_kwargs) 

# 需要導入模塊: from skopt import space [as 別名]
# 或者: from skopt.space import Integer [as 別名]
def test_searchcv_runs(surrogate, n_jobs, n_points, cv=None):
    """
    Test whether the cross validation search wrapper around sklearn
    models runs properly with available surrogates and with single
    or multiple workers and different number of parameter settings
    to ask from the optimizer in parallel.

    Parameters
    ----------

    * `surrogate` [str or None]:
        A class of the scikit-optimize surrogate used. None means
        to use default surrogate.

    * `n_jobs` [int]:
        Number of parallel processes to use for computations.

    """

    X, y = load_iris(True)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, train_size=0.75, random_state=0
    )

    # create an instance of a surrogate if it is not a string
    if surrogate is not None:
        optimizer_kwargs = {'base_estimator': surrogate}
    else:
        optimizer_kwargs = None

    opt = BayesSearchCV(
        SVC(),
        {
            'C': Real(1e-6, 1e+6, prior='log-uniform'),
            'gamma': Real(1e-6, 1e+1, prior='log-uniform'),
            'degree': Integer(1, 8),
            'kernel': Categorical(['linear', 'poly', 'rbf']),
        },
        n_jobs=n_jobs, n_iter=11, n_points=n_points, cv=cv,
        optimizer_kwargs=optimizer_kwargs
    )

    opt.fit(X_train, y_train)

    # this normally does not hold only if something is wrong
    # with the optimizaiton procedure as such
    assert opt.score(X_test, y_test) > 0.9