class_docstr: `UMAPOutlierDetection` is an outlier detector based on the reconstruction error from UMAP.
  
  If the difference between original and reconstructed data is larger than the `threshold`, the point is
      considered an outlier.
  
  Parameters
  ----------
  n_components : int, default=2
      Number of components of the UMAP model.
  threshold : float | None, default=None
      The threshold used for the decision function.
  variant : Literal["relative", "absolute"], default="relative"
      The variant used for the difference calculation. "relative" means that the difference between original and
      reconstructed data is divided by the sum of the original data.
  n_neighbors : int, default=15
      `n_neighbors` parameter of UMAP model.
  min_dist : float, default=0.1
      `min_dist` parameter of UMAP model.
  metric : str, default="euclidean"
      `metric` parameter of UMAP model
      (see [UMAP documentation](https://umap-learn.readthedocs.io/en/latest/parameters.html#metric) for the full list
      of available metrics and more information).
  random_state : int | None, default=None
      `random_state` parameter of UMAP model.
  
  Attributes
  ----------
  umap_ : UMAP
      The underlying UMAP model.
  offset_ : float
      The offset used for the decision function.
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docstr: Transform the data using the underlying UMAP method.
docstr_tok: ["Transform","the","data","using","the","underlying","UMAP","method","."]
code: def transform(self, X):
      """Transform the data using the underlying UMAP method."""
      X = check_array(X, estimator=self, dtype=FLOAT_DTYPES)
      check_is_fitted(self, ["umap_", "offset_"])
      return self.umap_.transform(X)
  
file_path: scikit-lego/sklego/decomposition/umap_reconstruction.py
imports: import numpy
  from sklearn.base import BaseEstimator, OutlierMixin
  from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted
label_desc: Use image node_id 3 for calling the UMAPOutlierDetection obj's underlying member method code with example usage: obj.transform(X) and returns: self