pyspark.ml.feature.StandardScaler(*, withMean: bool = False, withStd: bool = True, inputCol: Optional[str] = None, outputCol: Optional[str] = None)¶
Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.
The âunit stdâ is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.
Examples
>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
>>> standardScaler = StandardScaler()
>>> standardScaler.setInputCol("a")
StandardScaler...
>>> standardScaler.setOutputCol("scaled")
StandardScaler...
>>> model = standardScaler.fit(df)
>>> model.getInputCol()
'a'
>>> model.setOutputCol("output")
StandardScalerModel...
>>> model.mean
DenseVector([1.0])
>>> model.std
DenseVector([1.4142])
>>> model.transform(df).collect()[1].output
DenseVector([1.4142])
>>> standardScalerPath = temp_path + "/standard-scaler"
>>> standardScaler.save(standardScalerPath)
>>> loadedStandardScaler = StandardScaler.load(standardScalerPath)
>>> loadedStandardScaler.getWithMean() == standardScaler.getWithMean()
True
>>> loadedStandardScaler.getWithStd() == standardScaler.getWithStd()
True
>>> modelPath = temp_path + "/standard-scaler-model"
>>> model.save(modelPath)
>>> loadedModel = StandardScalerModel.load(modelPath)
>>> loadedModel.std == model.std
True
>>> loadedModel.mean == model.mean
True
>>> loadedModel.transform(df).take(1) == model.transform(df).take(1)
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.
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.
fit(dataset[, params])
Fits a model to the input dataset with optional parameters.
fitMultiple(dataset, paramMaps)
Fits a model to the input dataset for each param map in paramMaps.
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.
Gets the value of outputCol or its default value.
getParam(paramName)
Gets a param by its name.
Gets the value of withMean or its default value.
Gets the value of withStd 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 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.
setParams(self, \*[, withMean, withStd, â¦])
Sets params for this StandardScaler.
setWithMean(value)
Sets the value of withMean.
setWithStd(value)
Sets the value of withStd.
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.
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.
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.
extra param values
merged param map
fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]¶
Fits a model to the input dataset with optional parameters.
pyspark.sql.DataFrame
input dataset.
an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.
Transformer or a list of Transformer
fitted model(s)
fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]¶
Fits a model to the input dataset for each param map in paramMaps.
pyspark.sql.DataFrame
input dataset.
collections.abc.Sequence
A Sequence of param maps.
_FitMultipleIterator
A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.
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.
getOutputCol() → str¶
Gets the value of outputCol or its default value.
getParam(paramName: str) → pyspark.ml.param.Param¶
Gets a param by its name.
getWithMean() → bool¶
Gets the value of withMean or its default value.
getWithStd() → bool¶
Gets the value of withStd 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.
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) → pyspark.ml.feature.StandardScaler¶
Sets the value of inputCol.
setOutputCol(value: str) → pyspark.ml.feature.StandardScaler¶
Sets the value of outputCol.
setParams(self, \*, withMean=False, withStd=True, inputCol=None, outputCol=None)¶
Sets params for this StandardScaler.
setWithMean(value: bool) → pyspark.ml.feature.StandardScaler¶
Sets the value of withMean.
setWithStd(value: bool) → pyspark.ml.feature.StandardScaler¶
Sets the value of withStd.
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.')¶
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.
withMean = Param(parent='undefined', name='withMean', doc='Center data with mean')¶
withStd = Param(parent='undefined', name='withStd', doc='Scale to unit standard deviation')¶
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