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Showing content from https://api-docs.databricks.com/python/pyspark/latest/api/pyspark.ml.feature.VectorSizeHint.html below:

VectorSizeHint — PySpark master documentation

class pyspark.ml.feature.VectorSizeHint(*, inputCol: Optional[str] = None, size: Optional[int] = None, handleInvalid: str = 'error')¶

A feature transformer that adds size information to the metadata of a vector column. VectorAssembler needs size information for its input columns and cannot be used on streaming dataframes without this metadata.

Notes

VectorSizeHint modifies inputCol to include size metadata and does not have an outputCol.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml import Pipeline, PipelineModel
>>> data = [(Vectors.dense([1., 2., 3.]), 4.)]
>>> df = spark.createDataFrame(data, ["vector", "float"])
>>>
>>> sizeHint = VectorSizeHint(inputCol="vector", size=3, handleInvalid="skip")
>>> vecAssembler = VectorAssembler(inputCols=["vector", "float"], outputCol="assembled")
>>> pipeline = Pipeline(stages=[sizeHint, vecAssembler])
>>>
>>> pipelineModel = pipeline.fit(df)
>>> pipelineModel.transform(df).head().assembled
DenseVector([1.0, 2.0, 3.0, 4.0])
>>> vectorSizeHintPath = temp_path + "/vector-size-hint-pipeline"
>>> pipelineModel.save(vectorSizeHintPath)
>>> loadedPipeline = PipelineModel.load(vectorSizeHintPath)
>>> loaded = loadedPipeline.transform(df).head().assembled
>>> expected = pipelineModel.transform(df).head().assembled
>>> loaded == expected
True

Methods

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.

explainParams()

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.

getHandleInvalid()

Gets the value of handleInvalid or its default value.

getInputCol()

Gets the value of inputCol or its default value.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value.

getParam(paramName)

Gets a param by its name.

getSize()

Gets size param, the size of vectors in inputCol.

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 ‘write().save(path)’.

set(param, value)

Sets a parameter in the embedded param map.

setHandleInvalid(value)

Sets the value of handleInvalid.

setInputCol(value)

Sets the value of inputCol.

setParams(self, \*[, inputCol, size, …])

Sets params for this VectorSizeHint.

setSize(value)

Sets size param, the size of vectors in inputCol.

transform(dataset[, params])

Transforms the input dataset with optional parameters.

write()

Returns an MLWriter instance for this ML instance.

Attributes

Methods Documentation

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.

Parameters

extradict, optional

Extra parameters to copy to the new instance

Returns

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.

Parameters

extradict, optional

extra param values

Returns

dict

merged param map

getHandleInvalid() → str¶

Gets the value of handleInvalid or its default value.

getInputCol() → str¶

Gets the value of inputCol 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.

getParam(paramName: str) → pyspark.ml.param.Param¶

Gets a param by its name.

getSize() → int¶

Gets size param, the size of vectors in inputCol.

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.

classmethod load(path: str) → RL¶

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod 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 ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None¶

Sets a parameter in the embedded param map.

setHandleInvalid(value: str) → pyspark.ml.feature.VectorSizeHint¶

Sets the value of handleInvalid.

setInputCol(value: str) → pyspark.ml.feature.VectorSizeHint¶

Sets the value of inputCol.

setParams(self, \*, inputCol=None, size=None, handleInvalid="error")¶

Sets params for this VectorSizeHint.

setSize(value: int) → pyspark.ml.feature.VectorSizeHint¶

Sets size param, the size of vectors in inputCol.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶

Transforms the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame

transformed dataset

write() → pyspark.ml.util.JavaMLWriter¶

Returns an MLWriter instance for this ML instance.

Attributes Documentation

handleInvalid: pyspark.ml.param.Param[str] = Param(parent='undefined', name='handleInvalid', doc='How to handle invalid vectors in inputCol. Invalid vectors include nulls and vectors with the wrong size. The options are `skip` (filter out rows with invalid vectors), `error` (throw an error) and `optimistic` (do not check the vector size, and keep all rows). `error` by default.')¶

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')¶

params¶

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

size: pyspark.ml.param.Param[int] = Param(parent='undefined', name='size', doc='Size of vectors in column.')¶

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