VectorAssembler¶
-
class
pyspark.ml.feature.
VectorAssembler
(inputCols=None, outputCol=None, handleInvalid='error')[source]¶ A feature transformer that merges multiple columns into a vector column.
>>> df = spark.createDataFrame([(1, 0, 3)], ["a", "b", "c"]) >>> vecAssembler = VectorAssembler(outputCol="features") >>> vecAssembler.setInputCols(["a", "b", "c"]) VectorAssembler... >>> vecAssembler.transform(df).head().features DenseVector([1.0, 0.0, 3.0]) >>> vecAssembler.setParams(outputCol="freqs").transform(df).head().freqs DenseVector([1.0, 0.0, 3.0]) >>> params = {vecAssembler.inputCols: ["b", "a"], vecAssembler.outputCol: "vector"} >>> vecAssembler.transform(df, params).head().vector DenseVector([0.0, 1.0]) >>> vectorAssemblerPath = temp_path + "/vector-assembler" >>> vecAssembler.save(vectorAssemblerPath) >>> loadedAssembler = VectorAssembler.load(vectorAssemblerPath) >>> loadedAssembler.transform(df).head().freqs == vecAssembler.transform(df).head().freqs True >>> dfWithNullsAndNaNs = spark.createDataFrame( ... [(1.0, 2.0, None), (3.0, float("nan"), 4.0), (5.0, 6.0, 7.0)], ["a", "b", "c"]) >>> vecAssembler2 = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features", ... handleInvalid="keep") >>> vecAssembler2.transform(dfWithNullsAndNaNs).show() +---+---+----+-------------+ | a| b| c| features| +---+---+----+-------------+ |1.0|2.0|null|[1.0,2.0,NaN]| |3.0|NaN| 4.0|[3.0,NaN,4.0]| |5.0|6.0| 7.0|[5.0,6.0,7.0]| +---+---+----+-------------+ ... >>> vecAssembler2.setParams(handleInvalid="skip").transform(dfWithNullsAndNaNs).show() +---+---+---+-------------+ | a| b| c| features| +---+---+---+-------------+ |5.0|6.0|7.0|[5.0,6.0,7.0]| +---+---+---+-------------+ ...
New in version 1.4.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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copy
(extra=None)¶ 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
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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extractParamMap
(extra=None)¶ 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
extra – extra param values
- Returns
merged param map
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getHandleInvalid
()¶ Gets the value of handleInvalid or its default value.
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getInputCols
()¶ Gets the value of inputCols or its default value.
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getOrDefault
(param)¶ Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
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getOutputCol
()¶ Gets the value of outputCol or its default value.
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getParam
(paramName)¶ Gets a param by its name.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setHandleInvalid
(value)[source]¶ Sets the value of
handleInvalid
.
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setParams
(self, inputCols=None, outputCol=None, handleInvalid='error')[source]¶ Sets params for this VectorAssembler.
New in version 1.4.0.
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transform
(dataset, params=None)¶ Transforms the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.
- Returns
transformed dataset
New in version 1.3.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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handleInvalid
= Param(parent='undefined', name='handleInvalid', doc="How to handle invalid data (NULL and NaN values). Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), or 'keep' (return relevant number of NaN in the output). Column lengths are taken from the size of ML Attribute Group, which can be set using `VectorSizeHint` in a pipeline before `VectorAssembler`. Column lengths can also be inferred from first rows of the data since it is safe to do so but only in case of 'error' or 'skip').")¶
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inputCols
= Param(parent='undefined', name='inputCols', doc='input column names.')¶
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outputCol
= Param(parent='undefined', name='outputCol', doc='output column name.')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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