skhubness.neighbors.RandomProjectionTree¶
-
class
skhubness.neighbors.
RandomProjectionTree
(n_candidates: int = 5, metric: str = 'euclidean', n_trees: int = 10, search_k: int = -1, mmap_dir: str = 'auto', n_jobs: int = 1, verbose: int = 0)[source]¶ Wrapper for using annoy.AnnoyIndex
Annoy is an approximate nearest neighbor library, that builds a forest of random projections trees.
- Parameters
- n_candidates: int, default = 5
Number of neighbors to retrieve
- metric: str, default = ‘euclidean’
Distance metric, allowed are “angular”, “euclidean”, “manhattan”, “hamming”, “dot”
- n_trees: int, default = 10
Build a forest of n_trees trees. More trees gives higher precision when querying, but are more expensive in terms of build time and index size.
- search_k: int, default = -1
Query will inspect search_k nodes. A larger value will give more accurate results, but will take longer time.
- mmap_dir: str, default = ‘auto’
Memory-map the index to the given directory. This is required to make the the class pickleable. If None, keep everything in main memory (NON pickleable index), if mmap_dir is a string, it is interpreted as a directory to store the index into, if ‘auto’, create a temp dir for the index, preferably in /dev/shm on Linux.
- n_jobs: int, default = 1
Number of parallel jobs
- verbose: int, default = 0
Verbosity level. If verbose > 0, show tqdm progress bar on indexing and querying.
- Attributes
- valid_metrics:
List of valid distance metrics/measures
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__init__
(self, n_candidates: 'int' = 5, metric: 'str' = 'euclidean', n_trees: 'int' = 10, search_k: 'int' = -1, mmap_dir: 'str' = 'auto', n_jobs: 'int' = 1, verbose: 'int' = 0)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(self, n_candidates, metric, …)Initialize self.
fit
(self, X[, y])Build the annoy.Index and insert data from X.
get_params
(self[, deep])Get parameters for this estimator.
kneighbors
(self[, X, n_candidates, …])Retrieve k nearest neighbors.
set_params
(self, \*\*params)Set the parameters of this estimator.
Attributes
valid_metrics
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fit
(self, X, y=None) → 'RandomProjectionTree'[source]¶ Build the annoy.Index and insert data from X.
- Parameters
- X: np.array
Data to be indexed
- y: any
Ignored
- Returns
- self: RandomProjectionTree
An instance of RandomProjectionTree with a built index
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get_params
(self, deep=True)¶ Get parameters for this estimator.
- Parameters
- deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
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kneighbors
(self, X=None, n_candidates=None, return_distance=True) → 'Union[Tuple[np.array, np.array], np.array]'[source]¶ Retrieve k nearest neighbors.
- Parameters
- X: np.array or None, optional, default = None
Query objects. If None, search among the indexed objects.
- n_candidates: int or None, optional, default = None
Number of neighbors to retrieve. If None, use the value passed during construction.
- return_distance: bool, default = True
If return_distance, will return distances and indices to neighbors. Else, only return the indices.
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set_params
(self, **params)¶ 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