BucketedRandomProjectionLSHModel
Related Docs:
object BucketedRandomProjectionLSHModel
| package feature
An alias for getOrDefault().
An alias for getOrDefault().
Overloaded method for approxNearestNeighbors.
Overloaded method for approxNearestNeighbors. Use "distCol" as default distCol.
Given a large dataset and an item, approximately find at most k items which have the closest distance to the item.
Given a large dataset and an item, approximately find at most k items which have the closest distance to the item. If the outputCol is missing, the method will transform the data; if the outputCol exists, it will use the outputCol. This allows caching of the transformed data when necessary.
The dataset to search for nearest neighbors of the key.
Feature vector representing the item to search for.
The maximum number of nearest neighbors.
Output column for storing the distance between each result row and the key.
A dataset containing at most k items closest to the key. A column "distCol" is added to show the distance between each row and the key.
This method is experimental and will likely change behavior in the next release.
Overloaded method for approxSimilarityJoin.
Overloaded method for approxSimilarityJoin. Use "distCol" as default distCol.
Join two dataset to approximately find all pairs of rows whose distance are smaller than the threshold.
Join two dataset to approximately find all pairs of rows whose distance are smaller than the threshold. If the outputCol is missing, the method will transform the data; if the outputCol exists, it will use the outputCol. This allows caching of the transformed data when necessary.
One of the datasets to join.
Another dataset to join.
The threshold for the distance of row pairs.
Output column for storing the distance between each result row and the key.
A joined dataset containing pairs of rows. The original rows are in columns "datasetA" and "datasetB", and a distCol is added to show the distance of each pair.
The length of each hash bucket, a larger bucket lowers the false negative rate.
The length of each hash bucket, a larger bucket lowers the false negative rate. The number of
buckets will be (max L2 norm of input vectors) / bucketLength.
If input vectors are normalized, 1-10 times of pow(numRecords, -1/inputDim) would be a reasonable value
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
Creates a copy of this instance with the same UID and some extra params.
Creates a copy of this instance with the same UID and some extra params.
Subclasses should implement this method and set the return type properly.
See defaultCopy().
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
the target instance, which should work with the same set of default Params as this source instance
extra params to be copied to the target's paramMap
the target instance with param values copied
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
Explains a param.
Explains a param.
input param, must belong to this instance.
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
Explains all params of this instance.
Explains all params of this instance. See explainParam().
extractParamMap with no extra values.
extractParamMap with no extra values.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
Gets the default value of a parameter.
Gets the default value of a parameter.
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
Gets a param by its name.
Gets a param by its name.
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
Indicates whether this Model has a corresponding parent.
Calculate the distance between two different hash Vectors.
Calculate the distance between two different hash Vectors.
One of the hash vector.
Another hash vector.
The distance between hash vectors x and y.
The hash function of LSH, mapping an input feature vector to multiple hash vectors.
The hash function of LSH, mapping an input feature vector to multiple hash vectors.
The mapping of LSH function.
Param for input column name.
Param for input column name.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
Calculate the distance between two different keys using the distance metric corresponding to the hashFunction.
Calculate the distance between two different keys using the distance metric corresponding to the hashFunction.
One input vector in the metric space.
One input vector in the metric space.
The distance between x and y.
Param for the number of hash tables used in LSH OR-amplification.
Param for the number of hash tables used in LSH OR-amplification.
LSH OR-amplification can be used to reduce the false negative rate. Higher values for this param lead to a reduced false negative rate, at the expense of added computational complexity.
Param for output column name.
Param for output column name.
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
The parent estimator that produced this model.
The parent estimator that produced this model.
For ensembles' component Models, this value can be null.
Saves this ML instance to the input path, a shortcut of write.save(path).
Saves this ML instance to the input path, a shortcut of write.save(path).
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault.
Annotating this with varargs can cause compilation failures due to a Scala compiler bug.
See SPARK-9268.
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
Sets a default value for a param.
Sets a default value for a param.
param to set the default value. Make sure that this param is initialized before this method gets called.
the default value
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
Transforms the input dataset.
Transforms the input dataset.
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
input dataset
additional parameters, overwrite embedded params
transformed dataset
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
input dataset
the first param pair, overwrite embedded params
other param pairs, overwrite embedded params
transformed dataset
:: DeveloperApi ::
:: DeveloperApi ::
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
Transform the Schema for LSH
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
:: Experimental ::
Model produced by BucketedRandomProjectionLSH, where multiple random vectors are stored. The vectors are normalized to be unit vectors and each vector is used in a hash function:
h_i(x) = floor(r_i.dot(x) / bucketLength)wherer_iis the i-th random unit vector. The number of buckets will be(max L2 norm of input vectors) / bucketLength.