pyspark.ml.feature.MinHashLSHModel(java_model: Optional[JavaObject] = None)¶
Model produced by MinHashLSH, where 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.
Notes
See Tom Bohman, Colin Cooper, and Alan Frieze. âMin-wise independent linear permutations.â Electronic Journal of Combinatorics 7 (2000): R26.
Methods
approxNearestNeighbors(dataset, key, â¦[, â¦])
Given a large dataset and an item, approximately find at most k items which have the closest distance to the item.
approxSimilarityJoin(datasetA, datasetB, â¦)
Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold.
clear(param)
Clears a param from the param map if it has been explicitly set.
copy([extra])
Creates a copy of this instance with the same uid and some extra params.
explainParam(param)
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([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 < user-supplied values < extra.
Gets the value of inputCol or its default value.
Gets the value of numHashTables or its default value.
getOrDefault(param)
Gets the value of a param in the user-supplied param map or its default value.
Gets the value of outputCol or its default value.
getParam(paramName)
Gets a param by its name.
hasDefault(param)
Checks whether a param has a default value.
hasParam(paramName)
Tests whether this instance contains a param with a given (string) name.
isDefined(param)
Checks whether a param is explicitly set by user or has a default value.
isSet(param)
Checks whether a param is explicitly set by user.
load(path)
Reads an ML instance from the input path, a shortcut of read().load(path).
read()
Returns an MLReader instance for this class.
save(path)
Save this ML instance to the given path, a shortcut of âwrite().save(path)â.
set(param, value)
Sets a parameter in the embedded param map.
setInputCol(value)
Sets the value of inputCol.
setOutputCol(value)
Sets the value of outputCol.
transform(dataset[, params])
Transforms the input dataset with optional parameters.
write()
Returns an MLWriter instance for this ML instance.
Attributes
Methods Documentation
approxNearestNeighbors(dataset: pyspark.sql.dataframe.DataFrame, key: pyspark.ml.linalg.Vector, numNearestNeighbors: int, distCol: str = 'distCol') → pyspark.sql.dataframe.DataFrame¶
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 that. This allows caching of the transformed data when necessary.
pyspark.sql.DataFrame
The dataset to search for nearest neighbors of the key.
pyspark.ml.linalg.Vector
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. Use âdistColâ as default value if itâs not specified.
pyspark.sql.DataFrame
A dataset containing at most k items closest to the key. A column âdistColâ is added to show the distance between each row and the key.
Notes
This method is experimental and will likely change behavior in the next release.
approxSimilarityJoin(datasetA: pyspark.sql.dataframe.DataFrame, datasetB: pyspark.sql.dataframe.DataFrame, threshold: float, distCol: str = 'distCol') → pyspark.sql.dataframe.DataFrame¶
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 that. This allows caching of the transformed data when necessary.
pyspark.sql.DataFrame
One of the datasets to join.
pyspark.sql.DataFrame
Another dataset to join.
The threshold for the distance of row pairs.
Output column for storing the distance between each pair of rows. Use âdistColâ as default value if itâs not specified.
pyspark.sql.DataFrame
A joined dataset containing pairs of rows. The original rows are in columns âdatasetAâ and âdatasetBâ, and a column âdistColâ is added to show the distance between each pair.
clear(param: pyspark.ml.param.Param) → None¶
Clears a param from the param map if it has been explicitly set.
copy(extra: Optional[ParamMap] = None) → JP¶
Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.
Extra parameters to copy to the new instance
JavaParams
Copy of this instance
explainParam(param: Union[str, pyspark.ml.param.Param]) → str¶
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams() → str¶
Returns the documentation of all params with their optionally default values and user-supplied 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 < user-supplied values < extra.
extra param values
merged param map
getInputCol() → str¶
Gets the value of inputCol or its default value.
getNumHashTables() → int¶
Gets the value of numHashTables or its default value.
getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
getOutputCol() → str¶
Gets the value of outputCol or its default value.
getParam(paramName: str) → pyspark.ml.param.Param¶
Gets a param by its name.
hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶
Checks whether a param has a default value.
hasParam(paramName: str) → bool¶
Tests whether this instance contains a param with a given (string) name.
isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶
Checks whether a param is explicitly set by user or has a default value.
isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶
Checks whether a param is explicitly set by user.
load(path: str) → RL¶
Reads an ML instance from the input path, a shortcut of read().load(path).
read() → pyspark.ml.util.JavaMLReader[RL]¶
Returns an MLReader instance for this class.
save(path: str) → None¶
Save this ML instance to the given path, a shortcut of âwrite().save(path)â.
set(param: pyspark.ml.param.Param, value: Any) → None¶
Sets a parameter in the embedded param map.
setInputCol(value: str) → P¶
Sets the value of inputCol.
setOutputCol(value: str) → P¶
Sets the value of outputCol.
transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶
Transforms the input dataset with optional parameters.
pyspark.sql.DataFrame
input dataset
an optional param map that overrides embedded params.
pyspark.sql.DataFrame
transformed dataset
write() → pyspark.ml.util.JavaMLWriter¶
Returns an MLWriter instance for this ML instance.
Attributes Documentation
inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')¶
numHashTables: pyspark.ml.param.Param[int] = Param(parent='undefined', name='numHashTables', doc='number of hash tables, where increasing number of hash tables lowers the false negative rate, and decreasing it improves the running performance.')¶
outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')¶
params¶
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
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