pyspark.ml.feature.
SQLTransformer
Implements the transforms which are defined by SQL statement. Currently we only support SQL syntax like SELECT … FROM __THIS__ where __THIS__ represents the underlying table of the input dataset.
New in version 1.6.0.
Examples
>>> df = spark.createDataFrame([(0, 1.0, 3.0), (2, 2.0, 5.0)], ["id", "v1", "v2"]) >>> sqlTrans = SQLTransformer( ... statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__") >>> sqlTrans.transform(df).head() Row(id=0, v1=1.0, v2=3.0, v3=4.0, v4=3.0) >>> sqlTransformerPath = temp_path + "/sql-transformer" >>> sqlTrans.save(sqlTransformerPath) >>> loadedSqlTrans = SQLTransformer.load(sqlTransformerPath) >>> loadedSqlTrans.getStatement() == sqlTrans.getStatement() True >>> loadedSqlTrans.transform(df).take(1) == sqlTrans.transform(df).take(1) True
Methods
clear(param)
clear
Clears a param from the param map if it has been explicitly set.
copy([extra])
copy
Creates a copy of this instance with the same uid and some extra params.
explainParam(param)
explainParam
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()
explainParams
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])
extractParamMap
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.
getOrDefault(param)
getOrDefault
Gets the value of a param in the user-supplied param map or its default value.
getParam(paramName)
getParam
Gets a param by its name.
getStatement()
getStatement
Gets the value of statement or its default value.
hasDefault(param)
hasDefault
Checks whether a param has a default value.
hasParam(paramName)
hasParam
Tests whether this instance contains a param with a given (string) name.
isDefined(param)
isDefined
Checks whether a param is explicitly set by user or has a default value.
isSet(param)
isSet
Checks whether a param is explicitly set by user.
load(path)
load
Reads an ML instance from the input path, a shortcut of read().load(path).
read()
read
Returns an MLReader instance for this class.
save(path)
save
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)
set
Sets a parameter in the embedded param map.
setParams(self, \*[, statement])
setParams
Sets params for this SQLTransformer.
setStatement(value)
setStatement
Sets the value of statement.
statement
transform(dataset[, params])
transform
Transforms the input dataset with optional parameters.
write()
write
Returns an MLWriter instance for this ML instance.
Attributes
params
Returns all params ordered by name.
Methods Documentation
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
extra param values
merged param map
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
New in version 1.3.0.
pyspark.sql.DataFrame
input dataset
an optional param map that overrides embedded params.
transformed dataset
Attributes Documentation
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
dir()
Param