Binarizer¶

class pyspark.ml.feature.Binarizer(*, threshold: float = 0.0, inputCol: Optional[str] = None, outputCol: Optional[str] = None, thresholds: Optional[List[float]] = None, inputCols: Optional[List[str]] = None, outputCols: Optional[List[str]] = None)[source]¶

Binarize a column of continuous features given a threshold. Since 3.0.0, Binarize 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 threshold parameter is used for single column usage, and thresholds is for multiple columns.

New in version 1.4.0.

Examples

>>> df = spark.createDataFrame([(0.5,)], ["values"])
>>> binarizer = Binarizer(threshold=1.0, inputCol="values", outputCol="features")
>>> binarizer.setThreshold(1.0)
Binarizer...
>>> binarizer.setInputCol("values")
Binarizer...
>>> binarizer.setOutputCol("features")
Binarizer...
>>> binarizer.transform(df).head().features
0.0
>>> binarizer.setParams(outputCol="freqs").transform(df).head().freqs
0.0
>>> params = {binarizer.threshold: -0.5, binarizer.outputCol: "vector"}
>>> binarizer.transform(df, params).head().vector
1.0
>>> binarizerPath = temp_path + "/binarizer"
>>> binarizer.save(binarizerPath)
>>> loadedBinarizer = Binarizer.load(binarizerPath)
>>> loadedBinarizer.getThreshold() == binarizer.getThreshold()
True
>>> loadedBinarizer.transform(df).take(1) == binarizer.transform(df).take(1)
True
>>> df2 = spark.createDataFrame([(0.5, 0.3)], ["values1", "values2"])
>>> binarizer2 = Binarizer(thresholds=[0.0, 1.0])
>>> binarizer2.setInputCols(["values1", "values2"]).setOutputCols(["output1", "output2"])
Binarizer...
>>> binarizer2.transform(df2).show()
+-------+-------+-------+-------+
|values1|values2|output1|output2|
+-------+-------+-------+-------+
|    0.5|    0.3|    1.0|    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.
`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.
`getThreshold`()	Gets the value of threshold or its default value.
`getThresholds`()	Gets the value of thresholds 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.
`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, \*[, threshold, inputCol, …])	Sets params for this Binarizer.
`setThreshold`(value)	Sets the value of `threshold`.
`setThresholds`(value)	Sets the value of `thresholds`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`inputCol`
`inputCols`
`outputCol`
`outputCols`
`params`	Returns all params ordered by name.
`threshold`
`thresholds`

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

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.

getThreshold() → float¶: Gets the value of threshold or its default value.

getThresholds() → List[float]¶: Gets the value of thresholds 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.

setInputCol(value: str) → pyspark.ml.feature.Binarizer [source]¶: Sets the value of inputCol.

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

New in version 3.0.0.

setOutputCol(value: str) → pyspark.ml.feature.Binarizer [source]¶: Sets the value of outputCol.

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

New in version 3.0.0.

setParams(self, \*, threshold=0.0, inputCol=None, outputCol=None, thresholds=None, inputCols=None, outputCols=None)[source]¶: Sets params for this Binarizer.

New in version 1.4.0.

setThreshold(value: float) → pyspark.ml.feature.Binarizer [source]¶: Sets the value of threshold.

New in version 1.4.0.

setThresholds(value: List[float]) → pyspark.ml.feature.Binarizer [source]¶: Sets the value of thresholds.

New in version 3.0.0.

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

Transforms the input dataset with optional parameters.

New in version 1.3.0.

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

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.

threshold: pyspark.ml.param.Param[float] = Param(parent='undefined', name='threshold', doc='Param for threshold used to binarize continuous features. The features greater than the threshold will be binarized to 1.0. The features equal to or less than the threshold will be binarized to 0.0')¶

thresholds: pyspark.ml.param.Param[List[float]] = Param(parent='undefined', name='thresholds', doc='Param for array of threshold used to binarize continuous features. This is for multiple columns input. If transforming multiple columns and thresholds is not set, but threshold is set, then threshold will be applied across all columns.')¶

TypeConverters

BucketedRandomProjectionLSH