How to use the pyod.utils.utility.invert_order function in pyod

# validate inputs X and y (optional)
        X = check_array(X)
        self._set_n_classes(y)

        self.detector_ = LocalOutlierFactor(n_neighbors=self.n_neighbors,
                                            algorithm=self.algorithm,
                                            leaf_size=self.leaf_size,
                                            metric=self.metric,
                                            p=self.p,
                                            metric_params=self.metric_params,
                                            contamination=self.contamination,
                                            n_jobs=self.n_jobs)
        self.detector_.fit(X=X, y=y)

        # Invert decision_scores_. Outliers comes with higher outlier scores
        self.decision_scores_ = invert_order(
            self.detector_.negative_outlier_factor_)
        self._process_decision_scores()
        return self

# Do not pass behaviour argument when sklearn version is < 0.20 or >0.21
        else:  # pragma: no cover
            self.detector_ = IsolationForest(n_estimators=self.n_estimators,
                                             max_samples=self.max_samples,
                                             contamination=self.contamination,
                                             max_features=self.max_features,
                                             bootstrap=self.bootstrap,
                                             n_jobs=self.n_jobs,
                                             random_state=self.random_state,
                                             verbose=self.verbose)

        self.detector_.fit(X=X, y=None, sample_weight=None)

        # invert decision_scores_. Outliers comes with higher outlier scores.
        self.decision_scores_ = invert_order(
            self.detector_.decision_function(X))
        self._process_decision_scores()
        return self

self.detector_ = OneClassSVM(kernel=self.kernel,
                                     degree=self.degree,
                                     gamma=self.gamma,
                                     coef0=self.coef0,
                                     tol=self.tol,
                                     nu=self.nu,
                                     shrinking=self.shrinking,
                                     cache_size=self.cache_size,
                                     verbose=self.verbose,
                                     max_iter=self.max_iter)
        self.detector_.fit(X=X, y=y, sample_weight=sample_weight,
                           **params)

        # invert decision_scores_. Outliers comes with higher outlier scores
        self.decision_scores_ = invert_order(
            self.detector_.decision_function(X))
        self._process_decision_scores()
        return self

# build the histograms for all dimensions
        for i in range(n_features):
            self.hist_[:, i], self.bin_edges_[:, i] = \
                np.histogram(X[:, i], bins=self.n_bins, density=True)
            # the sum of (width * height) should equal to 1
            assert (np.isclose(1, np.sum(
                self.hist_[:, i] * np.diff(self.bin_edges_[:, i])), atol=0.1))

        # outlier_scores = self._calculate_outlier_scores(X)
        outlier_scores = _calculate_outlier_scores(X, self.bin_edges_,
                                                   self.hist_,
                                                   self.n_bins,
                                                   self.alpha, self.tol)

        # invert decision_scores_. Outliers comes with higher outlier scores
        self.decision_scores_ = invert_order(np.sum(outlier_scores, axis=1))
        self._process_decision_scores()
        return self

X : numpy array of shape (n_samples, n_features)
            The training input samples. Sparse matrices are accepted only
            if they are supported by the base estimator.

        Returns
        -------
        anomaly_scores : numpy array of shape (n_samples,)
            The anomaly score of the input samples.
        """

        check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])

        # Invert outlier scores. Outliers comes with higher outlier scores
        # noinspection PyProtectedMember
        if _get_sklearn_version() > 19:
            return invert_order(self.detector_._score_samples(X))
        else:
            return invert_order(self.detector_._decision_function(X))

if they are supported by the base estimator.

        Returns
        -------
        anomaly_scores : numpy array of shape (n_samples,)
            The anomaly score of the input samples.
        """
        check_is_fitted(self, ['hist_', 'bin_edges_'])
        X = check_array(X)

        # outlier_scores = self._calculate_outlier_scores(X)
        outlier_scores = _calculate_outlier_scores(X, self.bin_edges_,
                                                   self.hist_,
                                                   self.n_bins,
                                                   self.alpha, self.tol)
        return invert_order(np.sum(outlier_scores, axis=1))

larger anomaly scores.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The training input samples. Sparse matrices are accepted only
            if they are supported by the base estimator.

        Returns
        -------
        anomaly_scores : numpy array of shape (n_samples,)
            The anomaly score of the input samples.
        """
        check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
        # invert outlier scores. Outliers comes with higher outlier scores
        return invert_order(self.detector_.decision_function(X))

if they are supported by the base estimator.

        Returns
        -------
        anomaly_scores : numpy array of shape (n_samples,)
            The anomaly score of the input samples.
        """

        check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])

        # Invert outlier scores. Outliers comes with higher outlier scores
        # noinspection PyProtectedMember
        if _get_sklearn_version() > 19:
            return invert_order(self.detector_._score_samples(X))
        else:
            return invert_order(self.detector_._decision_function(X))

larger anomaly scores.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The training input samples. Sparse matrices are accepted only
            if they are supported by the base estimator.

        Returns
        -------
        anomaly_scores : numpy array of shape (n_samples,)
            The anomaly score of the input samples.
        """
        check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
        # Invert outlier scores. Outliers comes with higher outlier scores
        return invert_order(self.detector_.decision_function(X))