sklearn.ensemble
.RandomTreesEmbedding¶

class
sklearn.ensemble.
RandomTreesEmbedding
(n_estimators=’warn’, 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, min_impurity_split=None, sparse_output=True, n_jobs=None, random_state=None, verbose=0, warm_start=False)[source]¶ An ensemble of totally random trees.
An unsupervised transformation of a dataset to a highdimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a onehot 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
. Ifmax_leaf_nodes == None
, the number of leaf nodes is at mostn_estimators * 2 ** max_depth
.Read more in the User Guide.
Parameters:  n_estimators : integer, optional (default=10)
Number of trees in the forest.
Changed in version 0.20: The default value of
n_estimators
will change from 10 in version 0.20 to 100 in version 0.22. max_depth : integer, optional (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, float, optional (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 andceil(min_samples_split * n_samples)
is the minimum number of samples for each split.
Changed in version 0.18: Added float values for fractions.
 If int, then consider
 min_samples_leaf : int, float, optional (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 andceil(min_samples_leaf * n_samples)
is the minimum number of samples for each node.
Changed in version 0.18: Added float values for fractions.
 If int, then consider
 min_weight_fraction_leaf : float, optional (default=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 or None, optional (default=None)
Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decrease : float, optional (default=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, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
 min_impurity_split : float, (default=1e7)
Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.
Deprecated since version 0.19:
min_impurity_split
has been deprecated in favor ofmin_impurity_decrease
in 0.19. The default value ofmin_impurity_split
will change from 1e7 to 0 in 0.23 and it will be removed in 0.25. Usemin_impurity_decrease
instead. 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 : int or None, optional (default=None)
The number of jobs to run in parallel for both
fit
andpredict
.None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. 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 when fitting and predicting.
 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. See the Glossary.
Attributes:  estimators_ : list of DecisionTreeClassifier
The collection of fitted subestimators.
References
[1] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 342, 2006. [2] Moosmann, F. and Triggs, B. and Jurie, F. “Fast discriminative visual codebooks using randomized clustering forests” NIPS 2007 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_params
([deep])Get parameters for this estimator. set_params
(**params)Set the parameters of this estimator. transform
(X)Transform dataset. 
__init__
(n_estimators=’warn’, 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, min_impurity_split=None, sparse_output=True, n_jobs=None, random_state=None, verbose=0, warm_start=False)[source]¶

apply
(X)[source]¶ Apply trees in the forest to X, return leaf indices.
Parameters:  X : arraylike or sparse matrix, 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 sparsecsr_matrix
.
Returns:  X_leaves : array_like, 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.

decision_path
(X)[source]¶ Return the decision path in the forest
New in version 0.18.
Parameters:  X : arraylike or sparse matrix, 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 sparsecsr_matrix
.
Returns:  indicator : sparse csr array, shape = [n_samples, n_nodes]
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes.
 n_nodes_ptr : array of size (n_estimators + 1, )
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator.

feature_importances_
¶  Return the feature importances (the higher, the more important the
 feature).
Returns:  feature_importances_ : array, 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)[source]¶ Fit estimator.
Parameters:  X : arraylike or sparse matrix, shape=(n_samples, n_features)
The input samples. Use
dtype=np.float32
for maximum efficiency. Sparse matrices are also supported, use sparsecsc_matrix
for maximum efficiency. sample_weight : arraylike, shape = [n_samples] or 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

fit_transform
(X, y=None, sample_weight=None)[source]¶ Fit estimator and transform dataset.
Parameters:  X : arraylike or sparse matrix, shape=(n_samples, n_features)
Input data used to build forests. Use
dtype=np.float32
for maximum efficiency. sample_weight : arraylike, shape = [n_samples] or 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, shape=(n_samples, n_out)
Transformed dataset.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters:  deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:  params : mapping of string to any
Parameter names mapped to their values.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns:  self

transform
(X)[source]¶ Transform dataset.
Parameters:  X : arraylike 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 sparsecsr_matrix
for maximum efficiency.
Returns:  X_transformed : sparse matrix, shape=(n_samples, n_out)
Transformed dataset.