NGram¶

class pyspark.ml.feature.NGram(*, n: int = 2, inputCol: Optional[str] = None, outputCol: Optional[str] = None)[source]¶

A feature transformer that converts the input array of strings into an array of n-grams. Null values in the input array are ignored. It returns an array of n-grams where each n-gram is represented by a space-separated string of words. When the input is empty, an empty array is returned. When the input array length is less than n (number of elements per n-gram), no n-grams are returned.

New in version 1.5.0.

Examples

>>> df = spark.createDataFrame([Row(inputTokens=["a", "b", "c", "d", "e"])])
>>> ngram = NGram(n=2)
>>> ngram.setInputCol("inputTokens")
NGram...
>>> ngram.setOutputCol("nGrams")
NGram...
>>> ngram.transform(df).head()
Row(inputTokens=['a', 'b', 'c', 'd', 'e'], nGrams=['a b', 'b c', 'c d', 'd e'])
>>> # Change n-gram length
>>> ngram.setParams(n=4).transform(df).head()
Row(inputTokens=['a', 'b', 'c', 'd', 'e'], nGrams=['a b c d', 'b c d e'])
>>> # Temporarily modify output column.
>>> ngram.transform(df, {ngram.outputCol: "output"}).head()
Row(inputTokens=['a', 'b', 'c', 'd', 'e'], output=['a b c d', 'b c d e'])
>>> ngram.transform(df).head()
Row(inputTokens=['a', 'b', 'c', 'd', 'e'], nGrams=['a b c d', 'b c d e'])
>>> # Must use keyword arguments to specify params.
>>> ngram.setParams("text")
Traceback (most recent call last):
    ...
TypeError: Method setParams forces keyword arguments.
>>> ngramPath = temp_path + "/ngram"
>>> ngram.save(ngramPath)
>>> loadedNGram = NGram.load(ngramPath)
>>> loadedNGram.getN() == ngram.getN()
True
>>> loadedNGram.transform(df).take(1) == ngram.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.
`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.
`getN`()	Gets the value of n 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.
`getParam`(paramName)	Gets a param by its name.
`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`.
`setN`(value)	Sets the value of `n`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, n, inputCol, outputCol])	Sets params for this NGram.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`inputCol`
`n`
`outputCol`
`params`	Returns all params ordered by name.

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.

getN() → int[source]¶: Gets the value of n or its default value.

New in version 1.5.0.

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.

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.NGram [source]¶: Sets the value of inputCol.

setN(value: int) → pyspark.ml.feature.NGram [source]¶: Sets the value of n.

New in version 1.5.0.

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

setParams(self, \*, n=2, inputCol=None, outputCol=None)[source]¶: Sets params for this NGram.

New in version 1.5.0.

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.')¶

n: pyspark.ml.param.Param[int] = Param(parent='undefined', name='n', doc='number of elements per n-gram (>=1)')¶

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.

MinMaxScalerModel Normalizer