Bucketizer¶

class pyspark.ml.feature.Bucketizer(*, splits: Optional[List[float]] = None, inputCol: Optional[str] = None, outputCol: Optional[str] = None, handleInvalid: str = 'error', splitsArray: Optional[List[List[float]]] = None, inputCols: Optional[List[str]] = None, outputCols: Optional[List[str]] = None)¶

Maps a column of continuous features to a column of feature buckets. Since 3.0.0, Bucketizer can map multiple columns at once by setting the inputCols parameter. Note that when both the inputCol and inputCols parameters are set, an Exception will be thrown. The splits parameter is only used for single column usage, and splitsArray is for multiple columns.

Examples

>>> values = [(0.1, 0.0), (0.4, 1.0), (1.2, 1.3), (1.5, float("nan")),
...     (float("nan"), 1.0), (float("nan"), 0.0)]
>>> df = spark.createDataFrame(values, ["values1", "values2"])
>>> bucketizer = Bucketizer()
>>> bucketizer.setSplits([-float("inf"), 0.5, 1.4, float("inf")])
Bucketizer...
>>> bucketizer.setInputCol("values1")
Bucketizer...
>>> bucketizer.setOutputCol("buckets")
Bucketizer...
>>> bucketed = bucketizer.setHandleInvalid("keep").transform(df).collect()
>>> bucketed = bucketizer.setHandleInvalid("keep").transform(df.select("values1"))
>>> bucketed.show(truncate=False)
+-------+-------+
|values1|buckets|
+-------+-------+
|0.1    |0.0    |
|0.4    |0.0    |
|1.2    |1.0    |
|1.5    |2.0    |
|NaN    |3.0    |
|NaN    |3.0    |
+-------+-------+
...
>>> bucketizer.setParams(outputCol="b").transform(df).head().b
0.0
>>> bucketizerPath = temp_path + "/bucketizer"
>>> bucketizer.save(bucketizerPath)
>>> loadedBucketizer = Bucketizer.load(bucketizerPath)
>>> loadedBucketizer.getSplits() == bucketizer.getSplits()
True
>>> loadedBucketizer.transform(df).take(1) == bucketizer.transform(df).take(1)
True
>>> bucketed = bucketizer.setHandleInvalid("skip").transform(df).collect()
>>> len(bucketed)
4
>>> bucketizer2 = Bucketizer(splitsArray=
...     [[-float("inf"), 0.5, 1.4, float("inf")], [-float("inf"), 0.5, float("inf")]],
...     inputCols=["values1", "values2"], outputCols=["buckets1", "buckets2"])
>>> bucketed2 = bucketizer2.setHandleInvalid("keep").transform(df)
>>> bucketed2.show(truncate=False)
+-------+-------+--------+--------+
|values1|values2|buckets1|buckets2|
+-------+-------+--------+--------+
|0.1    |0.0    |0.0     |0.0     |
|0.4    |1.0    |0.0     |1.0     |
|1.2    |1.3    |1.0     |1.0     |
|1.5    |NaN    |2.0     |2.0     |
|NaN    |1.0    |3.0     |1.0     |
|NaN    |0.0    |3.0     |0.0     |
+-------+-------+--------+--------+
...

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.
`getInputCols`()	Gets the value of inputCols or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getOutputCol`()	Gets the value of outputCol or its default value.
`getOutputCols`()	Gets the value of outputCols or its default value.
`getParam`(paramName)	Gets a param by its name.
`getSplits`()	Gets the value of threshold or its default value.
`getSplitsArray`()	Gets the array of split points or its default value.
`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`.
`setInputCols`(value)	Sets the value of `inputCols`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setOutputCols`(value)	Sets the value of `outputCols`.
`setParams`(self, \*[, splits, inputCol, …])	Sets params for this Bucketizer.
`setSplits`(value)	Sets the value of `splits`.
`setSplitsArray`(value)	Sets the value of `splitsArray`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`handleInvalid`
`inputCol`
`inputCols`
`outputCol`
`outputCols`
`params`	Returns all params ordered by name.
`splits`
`splitsArray`

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.

extractParamMap(extra: Optional[ParamMap] = None) → 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 < 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.

getInputCols() → List[str]¶: Gets the value of inputCols 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.

getOutputCols() → List[str]¶: Gets the value of outputCols or its default value.

getParam(paramName: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

getSplits() → List[float]¶: Gets the value of threshold or its default value.

getSplitsArray() → List[List[float]]¶: Gets the array of split points or its default value.

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.Bucketizer ¶: Sets the value of handleInvalid.

setInputCol(value: str) → pyspark.ml.feature.Bucketizer ¶: Sets the value of inputCol.

setInputCols(value: List[str]) → pyspark.ml.feature.Bucketizer ¶: Sets the value of inputCols.

setOutputCol(value: str) → pyspark.ml.feature.Bucketizer ¶: Sets the value of outputCol.

setOutputCols(value: List[str]) → pyspark.ml.feature.Bucketizer ¶: Sets the value of outputCols.

setParams(self, \*, splits=None, inputCol=None, outputCol=None, handleInvalid="error", splitsArray=None, inputCols=None, outputCols=None)¶: Sets params for this Bucketizer.

setSplits(value: List[float]) → pyspark.ml.feature.Bucketizer ¶: Sets the value of splits.

setSplitsArray(value: List[List[float]]) → pyspark.ml.feature.Bucketizer ¶: Sets the value of splitsArray.

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 entries containing NaN values. Values outside the splits will always be treated as errors. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Note that in the multiple column case, the invalid handling is applied to all columns. That said for 'error' it will throw an error if any invalids are found in any column, for 'skip' it will skip rows with any invalids in any columns, etc.")¶

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

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

outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')¶

outputCols = Param(parent='undefined', name='outputCols', doc='output column names.')¶

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

splits: pyspark.ml.param.Param[List[float]] = Param(parent='undefined', name='splits', doc='Split points for mapping continuous features into buckets. With n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. The splits should be of length >= 3 and strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; otherwise, values outside the splits specified will be treated as errors.')¶

splitsArray: pyspark.ml.param.Param[List[List[float]]] = Param(parent='undefined', name='splitsArray', doc='The array of split points for mapping continuous features into buckets for multiple columns. For each input column, with n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. The splits should be of length >= 3 and strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; otherwise, values outside the splits specified will be treated as errors.')¶

BucketedRandomProjectionLSHModel

ChiSqSelector