Python sklearn.base.is_regressor() Examples

def base_estimator(self, value):
        # Build `base_estimator` if string given
        if isinstance(value, str):
            value = cook_estimator(
                value, space=self.space, random_state=self.rng.randint(0, np.iinfo(np.int32).max)
            )

        # Check if regressor
        if not is_regressor(value) and value is not None:
            raise ValueError(f"`base_estimator` must be a regressor. Got {value}")

        # Treat per second acquisition function specially
        is_multi_regressor = isinstance(value, MultiOutputRegressor)
        if self.acq_func.endswith("ps") and not is_multi_regressor:
            value = MultiOutputRegressor(value)

        self._base_estimator = value 

def __init__(self, models):
        """Proxy class to build an ensemble of models with an API as one

        Parameters
        ----------
        models: array
            An array of models
        """
        self._models = models if len(models) else None
        if self._models is not None:
            if is_classifier(self._models[0]):
                check_type = is_classifier
                self._scoring_fun = accuracy_score
            elif is_regressor(self._models[0]):
                check_type = is_regressor
                self._scoring_fun = r2_score
            else:
                raise ValueError('Expected regressors or classifiers,'
                                 ' got %s instead' % type(self._models[0]))
            for model in self._models:
                if not check_type(model):
                    raise ValueError('Different types of models found, privide'
                                     ' either regressors or classifiers.') 

def _check_final_regressor(self):
        if not is_regressor(self.final_regressor):
            raise ValueError(f"`final_regressor` should be a regressor, "
                             f"but found: {self.final_regressor}") 

def check_fit_idempotent(name, estimator_orig):
    # Check that est.fit(X) is the same as est.fit(X).fit(X). Ideally we would
    # check that the estimated parameters during training (e.g. coefs_) are
    # the same, but having a universal comparison function for those
    # attributes is difficult and full of edge cases. So instead we check that
    # predict(), predict_proba(), decision_function() and transform() return
    # the same results.

    check_methods = ["predict", "transform", "decision_function",
                     "predict_proba"]
    rng = np.random.RandomState(0)

    if estimator_orig._get_tags()['non_deterministic']:
        msg = name + ' is non deterministic'
        raise SkipTest(msg)

    estimator = clone(estimator_orig)
    set_random_state(estimator)
    if 'warm_start' in estimator.get_params().keys():
        estimator.set_params(warm_start=False)

    n_samples = 100
    X, _ = _create_small_ts_dataset()
    X = X.reshape((X.shape[0], X.shape[1]))
    X = pairwise_estimator_convert_X(X, estimator)
    if is_regressor(estimator_orig):
        y = rng.normal(size=n_samples)
    else:
        y = rng.randint(low=0, high=2, size=n_samples)

    train, test = next(ShuffleSplit(test_size=.2, random_state=rng).split(X))
    X_train, y_train = _safe_split(estimator, X, y, train)
    X_test, y_test = _safe_split(estimator, X, y, test, train)

    # Fit for the first time
    estimator.fit(X_train, y_train)

    result = {method: getattr(estimator, method)(X_test)
              for method in check_methods
              if hasattr(estimator, method)}

    # Fit again
    set_random_state(estimator)
    estimator.fit(X_train, y_train)

    for method in check_methods:
        if hasattr(estimator, method):
            new_result = getattr(estimator, method)(X_test)
            if np.issubdtype(new_result.dtype, np.floating):
                tol = 2*np.finfo(new_result.dtype).eps
            else:
                tol = 2*np.finfo(np.float64).eps
            assert_allclose_dense_sparse(
                result[method], new_result,
                atol=max(tol, 1e-9), rtol=max(tol, 1e-7),
                err_msg="Idempotency check failed for method {}".format(method)
            ) 

def yield_all_checks(name, estimator):
    tags = estimator._get_tags()
    if "2darray" not in tags["X_types"]:
        warnings.warn("Can't test estimator {} which requires input "
                      " of type {}".format(name, tags["X_types"]),
                      SkipTestWarning)
        return
    if tags["_skip_test"]:
        warnings.warn("Explicit SKIP via _skip_test tag for estimator "
                      "{}.".format(name),
                      SkipTestWarning)
        return

    yield from _yield_checks(name, estimator)
    if is_classifier(estimator):
        yield from _yield_classifier_checks(name, estimator)
    if is_regressor(estimator):
        yield from _yield_regressor_checks(name, estimator)
    if hasattr(estimator, 'transform'):
        if not tags["allow_variable_length"]:
            # Transformer tests ensure that shapes are the same at fit and
            # transform time, hence we need to skip them for estimators that
            # allow variable-length inputs
            yield from _yield_transformer_checks(name, estimator)
    if isinstance(estimator, ClusterMixin):
        yield from _yield_clustering_checks(name, estimator)
    if is_outlier_detector(estimator):
        yield from _yield_outliers_checks(name, estimator)
    # We are not strict on presence/absence of the 3rd dimension
    # yield check_fit2d_predict1d

    if not tags["non_deterministic"]:
        yield check_methods_subset_invariance

    yield check_fit2d_1sample
    yield check_fit2d_1feature
    yield check_fit1d
    yield check_get_params_invariance
    yield check_set_params
    yield check_dict_unchanged
    yield check_dont_overwrite_parameters
    yield check_fit_idempotent

    if (is_classifier(estimator) or
            is_regressor(estimator) or
            isinstance(estimator, ClusterMixin)):
        if tags["allow_variable_length"]:
            yield check_different_length_fit_predict_transform