sklearn.ensemble.RandomTreesEmbedding¶
- class sklearn.ensemble.RandomTreesEmbedding(n_estimators=10, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_leaf_nodes=None, sparse_output=True, n_jobs=1, random_state=None, verbose=0, warm_start=False)[source]¶
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.
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
[R136] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006. [R137] 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. 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=10, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_leaf_nodes=None, sparse_output=True, n_jobs=1, random_state=None, verbose=0, warm_start=False)[source]¶
- apply(X)[source]¶
Apply trees in the forest to X, return leaf indices.
Parameters: X : array-like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_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.
- feature_importances_¶
- Return the feature importances (the higher, the more important the
- feature).
Returns: feature_importances_ : array, shape = [n_features]
- fit(X, y=None, sample_weight=None)[source]¶
Fit estimator.
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.
- fit_transform(X, y=None, sample_weight=None)[source]¶
Fit estimator and transform dataset.
Parameters: X : array-like or sparse matrix, shape=(n_samples, n_features)
Input data used to build forests. Use dtype=np.float32 for maximum efficiency.
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 former 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 : 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.
