class MinHashLSHModel extends LSHModel[MinHashLSHModel]
Model produced by MinHashLSH, where multiple hash functions are stored. Each hash function
is picked from the following family of hash functions, where a_i and b_i are randomly chosen
integers less than prime:
h_i(x) = ((x \cdot a_i + b_i) \mod prime)
This hash family is approximately min-wise independent according to the reference.
Reference: Tom Bohman, Colin Cooper, and Alan Frieze. "Min-wise independent linear permutations." Electronic Journal of Combinatorics 7 (2000): R26.
- Annotations
- @Since( "2.1.0" )
- Source
- MinHashLSH.scala
- Grouped
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- MinHashLSHModel
- LSHModel
- MLWritable
- LSHParams
- HasOutputCol
- HasInputCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
final
val
inputCol: Param[String]
Param for input column name.
Param for input column name.
- Definition Classes
- HasInputCol
-
final
val
numHashTables: IntParam
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.
- Definition Classes
- LSHParams
-
final
val
outputCol: Param[String]
Param for output column name.
Param for output column name.
- Definition Classes
- HasOutputCol
Members
-
def
approxNearestNeighbors(dataset: Dataset[_], key: Vector, numNearestNeighbors: Int): Dataset[_]
Overloaded method for approxNearestNeighbors.
Overloaded method for approxNearestNeighbors. Use "distCol" as default distCol.
- Definition Classes
- LSHModel
-
def
approxNearestNeighbors(dataset: Dataset[_], key: Vector, numNearestNeighbors: Int, distCol: String): Dataset[_]
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.
- dataset
The dataset to search for nearest neighbors of the key.
- key
Feature vector representing the item to search for.
- numNearestNeighbors
The maximum number of nearest neighbors.
- distCol
Output column for storing the distance between each result row and the key.
- returns
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.
- Definition Classes
- LSHModel
- Note
This method is experimental and will likely change behavior in the next release.
-
def
approxSimilarityJoin(datasetA: Dataset[_], datasetB: Dataset[_], threshold: Double): Dataset[_]
Overloaded method for approxSimilarityJoin.
Overloaded method for approxSimilarityJoin. Use "distCol" as default distCol.
- Definition Classes
- LSHModel
-
def
approxSimilarityJoin(datasetA: Dataset[_], datasetB: Dataset[_], threshold: Double, distCol: String): Dataset[_]
Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold.
Join two datasets 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.
- datasetA
One of the datasets to join.
- datasetB
Another dataset to join.
- threshold
The threshold for the distance of row pairs.
- distCol
Output column for storing the distance between each pair of rows.
- returns
A joined dataset containing pairs of rows. The original rows are in columns "datasetA" and "datasetB", and a column "distCol" is added to show the distance between each pair.
- Definition Classes
- LSHModel
-
final
def
clear(param: Param[_]): MinHashLSHModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
def
copy(extra: ParamMap): MinHashLSHModel
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()
.- Definition Classes
- MinHashLSHModel → Model → Transformer → PipelineStage → Params
- Annotations
- @Since( "2.1.0" )
-
def
explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
-
def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam()
.- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
extractParamMap
with no extra values.extractParamMap
with no extra values.- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
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.
- Definition Classes
- Params
-
final
def
get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
-
final
def
getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
-
final
def
getOrDefault[T](param: Param[T]): T
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.
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
-
final
def
hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
-
def
hasParent: Boolean
Indicates whether this Model has a corresponding parent.
-
final
def
isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
- Definition Classes
- Params
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
lazy val
params: Array[Param[_]]
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.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
-
var
parent: Estimator[MinHashLSHModel]
The parent estimator that produced this model.
The parent estimator that produced this model.
- Definition Classes
- Model
- Note
For ensembles' component Models, this value can be null.
-
def
save(path: String): Unit
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)
.- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set[T](param: Param[T], value: T): MinHashLSHModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
setParent(parent: Estimator[MinHashLSHModel]): MinHashLSHModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
def
toString(): String
- Definition Classes
- MinHashLSHModel → Identifiable → AnyRef → Any
- Annotations
- @Since( "3.0.0" )
-
def
transform(dataset: Dataset[_]): DataFrame
Transforms the input dataset.
Transforms the input dataset.
- Definition Classes
- LSHModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
- dataset
input dataset
- paramMap
additional parameters, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
- dataset
input dataset
- firstParamPair
the first param pair, overwrite embedded params
- otherParamPairs
other param pairs, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
def
transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
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 byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- LSHModel → PipelineStage
-
val
uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- MinHashLSHModel → Identifiable
-
def
write: MLWriter
Returns an
MLWriter
instance for this ML instance.Returns an
MLWriter
instance for this ML instance.- Definition Classes
- MinHashLSHModel → MLWritable
- Annotations
- @Since( "2.1.0" )
Parameter setters
-
def
setInputCol(value: String): MinHashLSHModel.this.type
- Definition Classes
- MinHashLSHModel → LSHModel
- Annotations
- @Since( "2.4.0" )
-
def
setOutputCol(value: String): MinHashLSHModel.this.type
- Definition Classes
- MinHashLSHModel → LSHModel
- Annotations
- @Since( "2.4.0" )
Parameter getters
-
final
def
getInputCol: String
- Definition Classes
- HasInputCol
-
final
def
getNumHashTables: Int
- Definition Classes
- LSHParams
-
final
def
getOutputCol: String
- Definition Classes
- HasOutputCol