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# `sklearn.ensemble`.RandomTreesEmbedding

class sklearn.ensemble.RandomTreesEmbedding(n_estimators=100, *, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_leaf_nodes=None, min_impurity_decrease=0.0, sparse_output=True, n_jobs=None, random_state=None, verbose=0, warm_start=False)

  An ensemble of totally random trees.

  An unsupervised transformation of a dataset to a high-dimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a one-hot encoding of the leaves, this leads to a binary coding with as many ones as there are trees in the forest.

  The dimensionality of the resulting representation is `n_out <= n_estimators * max_leaf_nodes`. If `max_leaf_nodes == None`, the number of leaf nodes is at most `n_estimators * 2 ** max_depth`.

  Read more in the User Guide.

  Parameters:
    n_estimators int, default=100

      Number of trees in the forest.

      Changed in version 0.22: The default value of `n_estimators` changed from 10 to 100 in 0.22.

    max_depth int, default=5

      The maximum depth of each tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

    min_samples_split int or float, default=2

      The minimum number of samples required to split an internal node:

      - If int, then consider `min_samples_split` as the minimum number.

      - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` is the minimum number of samples for each split.

      Changed in version 0.18: Added float values for fractions.

    min_samples_leaf int or float, default=1

      The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least `min_samples_leaf` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

      - If int, then consider `min_samples_leaf` as the minimum number.

      - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` is the minimum number of samples for each node.

      Changed in version 0.18: Added float values for fractions.

    min_weight_fraction_leaf float, default=0.0

      The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

    max_leaf_nodes int, default=None

      Grow trees with `max_leaf_nodes` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

    min_impurity_decrease float, default=0.0

      A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

      The weighted impurity decrease equation is the following:

          N_t / N * (impurity - N_t_R / N_t * right_impurity
                              - N_t_L / N_t * left_impurity)

      where `N` is the total number of samples, `N_t` is the number of samples at the current node, `N_t_L` is the number of samples in the left child, and `N_t_R` is the number of samples in the right child.

      `N`, `N_t`, `N_t_R` and `N_t_L` all refer to the weighted sum, if `sample_weight` is passed.

      New in version 0.19.

    sparse_output bool, default=True

      Whether or not to return a sparse CSR matrix, as default behavior, or to return a dense array compatible with dense pipeline operators.

    n_jobs int, default=None

      The number of jobs to run in parallel. `fit`, `transform`, `decision_path` and `apply` are all parallelized over the trees. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

    random_state int, RandomState instance or None, default=None

      Controls the generation of the random `y` used to fit the trees and the draw of the splits for each feature at the trees’ nodes. See Glossary for details.

    verbose int, default=0

      Controls the verbosity when fitting and predicting.

    warm_start bool, default=False

      When set to `True`, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See Glossary and Fitting additional weak-learners for details.

  Attributes:
    estimator_`ExtraTreeRegressor` instance

      The child estimator template used to create the collection of fitted sub-estimators.

      New in version 1.2: `base_estimator_` was renamed to `estimator_`.

    `base_estimator_``ExtraTreeRegressor` instance

      Estimator used to grow the ensemble.

    estimators_list of `ExtraTreeRegressor` instances

      The collection of fitted sub-estimators.

    `feature_importances_`ndarray of shape (n_features,)

      The impurity-based feature importances.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

    n_outputs_int

      The number of outputs when `fit` is performed.

    one_hot_encoder_OneHotEncoder instance

      One-hot encoder used to create the sparse embedding.

  See also

  `ExtraTreesClassifier`

    An extra-trees classifier.

  `ExtraTreesRegressor`

    An extra-trees regressor.

  `RandomForestClassifier`

    A random forest classifier.

  `RandomForestRegressor`

    A random forest regressor.

  `sklearn.tree.ExtraTreeClassifier`

    An extremely randomized tree classifier.

  `sklearn.tree.ExtraTreeRegressor`

    An extremely randomized tree regressor.

  References

  [1]

  P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006.

  [2]

  Moosmann, F. and Triggs, B. and Jurie, F. “Fast discriminative visual codebooks using randomized clustering forests” NIPS 2007

  Examples

      >>> from sklearn.ensemble import RandomTreesEmbedding
      >>> X = [[0,0], [1,0], [0,1], [-1,0], [0,-1]]
      >>> random_trees = RandomTreesEmbedding(
      ...    n_estimators=5, random_state=0, max_depth=1).fit(X)
      >>> X_sparse_embedding = random_trees.transform(X)
      >>> X_sparse_embedding.toarray()
      array([[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
             [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
             [0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],
             [1., 0., 1., 0., 1., 0., 1., 0., 1., 0.],
             [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.]])

  Methods

  `apply`(X) | Apply trees in the forest to X, return leaf indices.
  `decision_path`(X) | Return the decision path in the forest.
  `fit`(X[, y, sample_weight]) | Fit estimator.
  `fit_transform`(X[, y, sample_weight]) | Fit estimator and transform dataset.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `set_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Transform dataset.

  apply(X)

    Apply trees in the forest to X, return leaf indices.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, its dtype will be converted to `dtype=np.float32`. If a sparse matrix is provided, it will be converted into a sparse `csr_matrix`.

    Returns:
      X_leaves ndarray of shape (n_samples, n_estimators)

        For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.

  property base_estimator_

    Estimator used to grow the ensemble.

  decision_path(X)

    Return the decision path in the forest.

    New in version 0.18.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, its dtype will be converted to `dtype=np.float32`. If a sparse matrix is provided, it will be converted into a sparse `csr_matrix`.

    Returns:
      indicator sparse matrix of shape (n_samples, n_nodes)

        Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.

      n_nodes_ptr ndarray of shape (n_estimators + 1,)

        The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the i-th estimator.

  property feature_importances_

    The impurity-based feature importances.

    The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.

    Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See `sklearn.inspection.permutation_importance` as an alternative.

    Returns:
      feature_importances_ndarray of shape (n_features,)

        The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros.

  fit(X, y=None, sample_weight=None)

    Fit estimator.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Use `dtype=np.float32` for maximum efficiency. Sparse matrices are also supported, use sparse `csc_matrix` for maximum efficiency.

      y Ignored

        Not used, present for API consistency by convention.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

    Returns:
      self object

        Returns the instance itself.

  fit_transform(X, y=None, sample_weight=None)

    Fit estimator and transform dataset.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Input data used to build forests. Use `dtype=np.float32` for maximum efficiency.

      y Ignored

        Not used, present for API consistency by convention.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

    Returns:
      X_transformed sparse matrix of shape (n_samples, n_out)

        Transformed dataset.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    Parameters:
      input_features array-like of str or None, default=None

        Only used to validate feature names with the names seen in `fit`.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names, in the format of `randomtreesembedding_{tree}_{leaf}`, where `tree` is the tree used to generate the leaf and `leaf` is the index of a leaf node in that tree. Note that the node indexing scheme is used to index both nodes with children (split nodes) and leaf nodes. Only the latter can be present as output features. As a consequence, there are missing indices in the output feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → RandomTreesEmbedding

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  transform(X)

    Transform dataset.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Input data to be transformed. Use `dtype=np.float32` for maximum efficiency. Sparse matrices are also supported, use sparse `csr_matrix` for maximum efficiency.

    Returns:
      X_transformed sparse matrix of shape (n_samples, n_out)

        Transformed dataset.

## Examples using `sklearn.ensemble.RandomTreesEmbedding`

Feature transformations with ensembles of trees

Feature transformations with ensembles of trees

Hashing feature transformation using Totally Random Trees

Hashing feature transformation using Totally Random Trees

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...