Class RandomTreesEmbeddingScikitsLearnNode

An ensemble of totally random trees.

This node has been automatically generated by wrapping the ``sklearn.ensemble.forest.RandomTreesEmbedding`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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 :ref:`User Guide <random_trees_embedding>`.

**Parameters**

n_estimators : int
    Number of trees in the forest.

max_depth : int
    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.
    Ignored if ``max_leaf_nodes`` is not None.

min_samples_split : integer, optional (default=2)
    The minimum number of samples required to split an internal node.

min_samples_leaf : integer, optional (default=1)
    The minimum number of samples in newly created leaves.  A split is
    discarded if after the split, one of the leaves would contain less then
    ``min_samples_leaf`` samples.

min_weight_fraction_leaf : float, optional (default=0.)
    The minimum weighted fraction of the input samples required to be at a
    leaf node.

max_leaf_nodes : int or None, optional (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.
    If not None then ``max_depth`` will be ignored.

sparse_output : bool, optional (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 : integer, optional (default=1)
    The number of jobs to run in parallel for both `fit` and `predict`.
    If -1, then the number of jobs is set to the number of cores.

random_state : int, RandomState instance or None, optional (default=None)
    If int, random_state is the seed used by the random number generator;
    If RandomState instance, random_state is the random number generator;
    If None, the random number generator is the RandomState instance used
    by `np.random`.

verbose : int, optional (default=0)
    Controls the verbosity of the tree building process.

warm_start : bool, optional (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.

**Attributes**

``estimators_`` : list of DecisionTreeClassifier
    The collection of fitted sub-estimators.

**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

Overrides: object.__init__

Transform dataset.

This node has been automatically generated by wrapping the sklearn.ensemble.forest.RandomTreesEmbedding class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like or sparse matrix, 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, shape=(n_samples, n_out)

Transformed dataset.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit estimator.

This node has been automatically generated by wrapping the sklearn.ensemble.forest.RandomTreesEmbedding class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like or sparse matrix, 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.

Returns

self : object

Returns self.

Overrides: Node.stop_training