def _from_java(cls, JavaObject):
sc = SparkContext._active_spark_context
bucket = _java2py(sc, JavaObject.bucket())
object_path = _java2py(sc, JavaObject.objectPath())
return S3DataPath(bucket, object_path)
def _transfer_params_from_java(self):
"""
Transforms the embedded com.microsoft.azure.synapse.ml.core.serialize.params from the companion Java object.
"""
sc = SparkContext._active_spark_context
for param in self.params:
if self._java_obj.hasParam(param.name):
java_param = self._java_obj.getParam(param.name)
# SPARK-14931: Only check set com.microsoft.azure.synapse.ml.core.serialize.params back to avoid default com.microsoft.azure.synapse.ml.core.serialize.params mismatch.
complex_param_class = sc._gateway.jvm.com.microsoft.azure.synapse.ml.core.serialize.ComplexParam._java_lang_class
is_complex_param = complex_param_class.isAssignableFrom(
java_param.getClass())
service_param_class = sc._gateway.jvm.org.apache.spark.ml.param.ServiceParam._java_lang_class
is_service_param = service_param_class.isAssignableFrom(
java_param.getClass())
if self._java_obj.isSet(java_param):
if is_complex_param:
value = self._java_obj.getOrDefault(java_param)
elif is_service_param:
jvObj = self._java_obj.getOrDefault(java_param)
if jvObj.isLeft():
value = _java2py(sc, jvObj.value())
else:
value = None
else:
value = _java2py(
sc, self._java_obj.getOrDefault(java_param))
self._set(**{param.name: value})
def meta_estimator_transfer_param_maps_from_java(pyEstimator, javaParamMaps):
pyStages = MetaAlgorithmReadWrite.getAllNestedStages(pyEstimator)
stagePairs = list(map(lambda stage: (stage, stage._to_java()), pyStages))
sc = SparkContext._active_spark_context
pyParamMaps = []
for javaParamMap in javaParamMaps:
pyParamMap = dict()
for javaPair in javaParamMap.toList():
javaParam = javaPair.param()
pyParam = None
for pyStage, javaStage in stagePairs:
if pyStage._testOwnParam(javaParam.parent(), javaParam.name()):
pyParam = pyStage.getParam(javaParam.name())
if pyParam is None:
raise ValueError('Resolve param in estimatorParamMaps failed: ' +
javaParam.parent() + '.' + javaParam.name())
javaValue = javaPair.value()
if sc._jvm.Class.forName("org.apache.spark.ml.util.DefaultParamsWritable") \
.isInstance(javaValue):
pyValue = JavaParams._from_java(javaValue)
else:
pyValue = _java2py(sc, javaValue)
pyParamMap[pyParam] = pyValue
pyParamMaps.append(pyParamMap)
return pyParamMaps
def _call_java(sc, java_obj, name, *args):
"""
Method copied from pyspark.ml.wrapper. Uses private Spark APIs.
"""
m = getattr(java_obj, name)
java_args = [_py2java(sc, arg) for arg in args]
return _java2py(sc, m(*java_args))
def _from_java(cls, java_stage):
"""
Given a Java CrossValidatorModel, create and return a Python wrapper of it.
Used for ML persistence.
"""
sc = SparkContext._active_spark_context
bestModel = JavaParams._from_java(java_stage.bestModel())
avgMetrics = _java2py(sc, java_stage.avgMetrics())
estimator, epms, evaluator = super(CrossValidatorModel,
cls)._from_java_impl(java_stage)
py_stage = cls(bestModel=bestModel, avgMetrics=avgMetrics)
params = {
"evaluator": evaluator,
"estimator": estimator,
"estimatorParamMaps": epms,
"numFolds": java_stage.getNumFolds(),
"foldCol": java_stage.getFoldCol(),
"seed": java_stage.getSeed(),
}
for param_name, param_val in params.items():
py_stage = py_stage._set(**{param_name: param_val})
if java_stage.hasSubModels():
py_stage.subModels = [[
JavaParams._from_java(sub_model)
for sub_model in fold_sub_models
] for fold_sub_models in java_stage.subModels()]
py_stage._resetUid(java_stage.uid())
return py_stage
def _call_java(sc, java_obj, name, *args):
"""
Method copied from pyspark.ml.wrapper. Uses private Spark APIs.
"""
m = getattr(java_obj, name)
java_args = [_py2java(sc, arg) for arg in args]
return _java2py(sc, m(*java_args))
def meta_estimator_transfer_param_maps_from_java(pyEstimator,
javaParamMaps):
pyStages = MetaAlgorithmReadWrite.getAllNestedStages(pyEstimator)
stagePairs = list(
map(lambda stage: (stage, stage._to_java()), pyStages))
sc = SparkContext._active_spark_context
pyParamMaps = []
for javaParamMap in javaParamMaps:
pyParamMap = dict()
for javaPair in javaParamMap.toList():
javaParam = javaPair.param()
pyParam = None
for pyStage, javaStage in stagePairs:
if pyStage._testOwnParam(javaParam.parent(),
javaParam.name()):
pyParam = pyStage.getParam(javaParam.name())
if pyParam is None:
raise ValueError(
'Resolve param in estimatorParamMaps failed: ' +
javaParam.parent() + '.' + javaParam.name())
javaValue = javaPair.value()
if sc._jvm.Class.forName("org.apache.spark.ml.PipelineStage"
).isInstance(javaValue):
# Note: JavaParams._from_java support both JavaEstimator/JavaTransformer class
# and Estimator/Transformer class which implements `_from_java` static method
# (such as OneVsRest, Pipeline class).
pyValue = JavaParams._from_java(javaValue)
else:
pyValue = _java2py(sc, javaValue)
pyParamMap[pyParam] = pyValue
pyParamMaps.append(pyParamMap)
return pyParamMaps
def test(dataset, featuresCol, labelCol):
"""
Perform a Pearson's independence test using dataset.
:param dataset:
DataFrame of categorical labels and categorical features.
Real-valued features will be treated as categorical for each distinct value.
:param featuresCol:
Name of features column in dataset, of type `Vector` (`VectorUDT`).
:param labelCol:
Name of label column in dataset, of any numerical type.
:return:
DataFrame containing the test result for every feature against the label.
This DataFrame will contain a single Row with the following fields:
- `pValues: Vector`
- `degreesOfFreedom: Array[Int]`
- `statistics: Vector`
Each of these fields has one value per feature.
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.stat import ChiSquareTest
>>> dataset = [[0, Vectors.dense([0, 0, 1])],
... [0, Vectors.dense([1, 0, 1])],
... [1, Vectors.dense([2, 1, 1])],
... [1, Vectors.dense([3, 1, 1])]]
>>> dataset = spark.createDataFrame(dataset, ["label", "features"])
>>> chiSqResult = ChiSquareTest.test(dataset, 'features', 'label')
>>> chiSqResult.select("degreesOfFreedom").collect()[0]
Row(degreesOfFreedom=[3, 1, 0])
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.ChiSquareTest
args = [_py2java(sc, arg) for arg in (dataset, featuresCol, labelCol)]
return _java2py(sc, javaTestObj.test(*args))
def _transfer_params_from_java(self):
"""
Transforms the embedded params from the companion Java object.
"""
sc = SparkContext._active_spark_context
for param in self.params:
if self._java_obj.hasParam(param.name):
java_param = self._java_obj.getParam(param.name)
# SPARK-14931: Only check set params back to avoid default params mismatch.
if self._java_obj.isSet(java_param):
value = _java2py(sc, self._java_obj.getOrDefault(java_param))
self._set(**{param.name: value})
# SPARK-10931: Temporary fix for params that have a default in Java
if self._java_obj.hasDefault(java_param) and not self.isDefined(param):
value = _java2py(sc, self._java_obj.getDefault(java_param)).get()
self._setDefault(**{param.name: value})
def _from_java(cls, java_stage):
"""
Given a Java TrainValidationSplitModel, create and return a Python wrapper of it.
Used for ML persistence.
"""
# Load information from java_stage to the instance.
sc = SparkContext._active_spark_context
bestModel = JavaParams._from_java(java_stage.bestModel())
validationMetrics = _java2py(sc, java_stage.validationMetrics())
estimator, epms, evaluator = super(TrainValidationSplitModel,
cls)._from_java_impl(java_stage)
# Create a new instance of this stage.
py_stage = cls(
bestModel=bestModel,
validationMetrics=validationMetrics)._set(estimator=estimator)
py_stage = py_stage._set(estimatorParamMaps=epms)._set(
evaluator=evaluator)
if java_stage.hasSubModels():
py_stage.subModels = [
JavaParams._from_java(sub_model)
for sub_model in java_stage.subModels()
]
py_stage._resetUid(java_stage.uid())
return py_stage
def _transfer_params_from_java(self):
"""
Transforms the embedded params from the companion Java object.
"""
sc = SparkContext._active_spark_context
for param in self.params:
if self._java_obj.hasParam(param.name):
java_param = self._java_obj.getParam(param.name)
# SPARK-14931: Only check set params back to avoid default params mismatch.
if self._java_obj.isSet(java_param):
value = _java2py(sc, self._java_obj.getOrDefault(java_param))
self._set(**{param.name: value})
# SPARK-10931: Temporary fix for params that have a default in Java
if self._java_obj.hasDefault(java_param) and not self.isDefined(param):
value = _java2py(sc, self._java_obj.getDefault(java_param)).get()
self._setDefault(**{param.name: value})
def _from_java(cls, java_stage):
"""
Given a Java TrainValidationSplitModel, create and return a Python wrapper of it.
Used for ML persistence.
"""
# Load information from java_stage to the instance.
sc = SparkContext._active_spark_context
bestModel = JavaParams._from_java(java_stage.bestModel())
validationMetrics = _java2py(sc, java_stage.validationMetrics())
estimator, epms, evaluator = super(TrainValidationSplitModel,
cls)._from_java_impl(java_stage)
# Create a new instance of this stage.
py_stage = cls(bestModel=bestModel,
validationMetrics=validationMetrics)
params = {
"evaluator": evaluator,
"estimator": estimator,
"estimatorParamMaps": epms,
"trainRatio": java_stage.getTrainRatio(),
"seed": java_stage.getSeed(),
}
for param_name, param_val in params.items():
py_stage = py_stage._set(**{param_name: param_val})
if java_stage.hasSubModels():
py_stage.subModels = [
JavaParams._from_java(sub_model)
for sub_model in java_stage.subModels()
]
py_stage._resetUid(java_stage.uid())
return py_stage
def _call_java(self, name: str, *args: Any) -> Any:
m = getattr(self._java_obj, name)
sc = SparkContext._active_spark_context
assert sc is not None
java_args = [_py2java(sc, arg) for arg in args]
return _java2py(sc, m(*java_args))
def test(dataset, featuresCol, labelCol):
"""
Perform a Pearson's independence test using dataset.
:param dataset:
DataFrame of categorical labels and categorical features.
Real-valued features will be treated as categorical for each distinct value.
:param featuresCol:
Name of features column in dataset, of type `Vector` (`VectorUDT`).
:param labelCol:
Name of label column in dataset, of any numerical type.
:return:
DataFrame containing the test result for every feature against the label.
This DataFrame will contain a single Row with the following fields:
- `pValues: Vector`
- `degreesOfFreedom: Array[Int]`
- `statistics: Vector`
Each of these fields has one value per feature.
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.stat import ChiSquareTest
>>> dataset = [[0, Vectors.dense([0, 0, 1])],
... [0, Vectors.dense([1, 0, 1])],
... [1, Vectors.dense([2, 1, 1])],
... [1, Vectors.dense([3, 1, 1])]]
>>> dataset = spark.createDataFrame(dataset, ["label", "features"])
>>> chiSqResult = ChiSquareTest.test(dataset, 'features', 'label')
>>> chiSqResult.select("degreesOfFreedom").collect()[0]
Row(degreesOfFreedom=[3, 1, 0])
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.ChiSquareTest
args = [_py2java(sc, arg) for arg in (dataset, featuresCol, labelCol)]
return _java2py(sc, javaTestObj.test(*args))
def corr(dataset, column, method="pearson"):
"""
Compute the correlation matrix with specified method using dataset.
"""
sc = SparkContext._active_spark_context
javaCorrObj = _jvm().org.apache.spark.ml.stat.Correlation
args = [_py2java(sc, arg) for arg in (dataset, column, method)]
return _java2py(sc, javaCorrObj.corr(*args))
def test(dataset, featuresCol, labelCol):
"""
Perform a Pearson's independence test using dataset.
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.ChiSquareTest
args = [_py2java(sc, arg) for arg in (dataset, featuresCol, labelCol)]
return _java2py(sc, javaTestObj.test(*args))
def corr(dataset, column, method="pearson"):
"""
Compute the correlation matrix with specified method using dataset.
"""
sc = SparkContext._active_spark_context
javaCorrObj = _jvm().org.apache.spark.ml.stat.Correlation
args = [_py2java(sc, arg) for arg in (dataset, column, method)]
return _java2py(sc, javaCorrObj.corr(*args))
def test(dataset, featuresCol, labelCol):
"""
Perform a Pearson's independence test using dataset.
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.ChiSquareTest
args = [_py2java(sc, arg) for arg in (dataset, featuresCol, labelCol)]
return _java2py(sc, javaTestObj.test(*args))
def test(dataset: DataFrame, sampleCol: str, distName: str,
*params: float) -> DataFrame:
"""
Conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution
equality. Currently supports the normal distribution, taking as parameters the mean and
standard deviation.
.. versionadded:: 2.4.0
Parameters
----------
dataset : :py:class:`pyspark.sql.DataFrame`
a Dataset or a DataFrame containing the sample of data to test.
sampleCol : str
Name of sample column in dataset, of any numerical type.
distName : str
a `string` name for a theoretical distribution, currently only support "norm".
params : float
a list of `float` values specifying the parameters to be used for the theoretical
distribution. For "norm" distribution, the parameters includes mean and variance.
Returns
-------
A DataFrame that contains the Kolmogorov-Smirnov test result for the input sampled data.
This DataFrame will contain a single Row with the following fields:
- `pValue: Double`
- `statistic: Double`
Examples
--------
>>> from pyspark.ml.stat import KolmogorovSmirnovTest
>>> dataset = [[-1.0], [0.0], [1.0]]
>>> dataset = spark.createDataFrame(dataset, ['sample'])
>>> ksResult = KolmogorovSmirnovTest.test(dataset, 'sample', 'norm', 0.0, 1.0).first()
>>> round(ksResult.pValue, 3)
1.0
>>> round(ksResult.statistic, 3)
0.175
>>> dataset = [[2.0], [3.0], [4.0]]
>>> dataset = spark.createDataFrame(dataset, ['sample'])
>>> ksResult = KolmogorovSmirnovTest.test(dataset, 'sample', 'norm', 3.0, 1.0).first()
>>> round(ksResult.pValue, 3)
1.0
>>> round(ksResult.statistic, 3)
0.175
"""
sc = SparkContext._active_spark_context
assert sc is not None
javaTestObj = _jvm().org.apache.spark.ml.stat.KolmogorovSmirnovTest
dataset = _py2java(sc, dataset)
params = [float(param) for param in params] # type: ignore[assignment]
return _java2py(
sc,
javaTestObj.test(dataset, sampleCol, distName,
_jvm().PythonUtils.toSeq(params)))
def _transfer_param_map_from_java(self, javaParamMap):
"""
Transforms a Java ParamMap into a Python ParamMap.
"""
sc = SparkContext._active_spark_context
paramMap = dict()
for pair in javaParamMap.toList():
param = pair.param()
if self.hasParam(str(param.name())):
paramMap[self.getParam(param.name())] = _java2py(sc, pair.value())
return paramMap
def test(dataset, featuresCol, labelCol, flatten=False):
"""
Perform a F Regression test using dataset.
:param dataset:
DataFrame of continuous labels and continuous features.
:param featuresCol:
Name of features column in dataset, of type `Vector` (`VectorUDT`).
:param labelCol:
Name of label column in dataset, of any numerical type.
:param flatten: if True, flattens the returned dataframe.
:return:
DataFrame containing the test result for every feature against the label.
If flatten is True, this DataFrame will contain one row per feature with the following
fields:
- `featureIndex: int`
- `pValue: float`
- `degreesOfFreedom: int`
- `fValue: float`
If flatten is False, this DataFrame will contain a single Row with the following fields:
- `pValues: Vector`
- `degreesOfFreedom: Array[int]`
- `fValues: Vector`
Each of these fields has one value per feature.
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.stat import FValueTest
>>> dataset = [[0.57495218, Vectors.dense([0.43486404, 0.57153633, 0.43175686,
... 0.51418671, 0.61632374, 0.96565515])],
... [0.84619853, Vectors.dense([0.49162732, 0.6785187, 0.85460572,
... 0.59784822, 0.12394819, 0.53783355])],
... [0.39777647, Vectors.dense([0.30879653, 0.54904515, 0.17103889,
... 0.40492506, 0.18957493, 0.5440016])],
... [0.79201573, Vectors.dense([0.68114391, 0.60549825, 0.69094651,
... 0.62102109, 0.05471483, 0.96449167])]]
>>> dataset = spark.createDataFrame(dataset, ["label", "features"])
>>> fValueResult = FValueTest.test(dataset, 'features', 'label')
>>> row = fValueResult.select("fValues", "pValues").collect()
>>> row[0].fValues
DenseVector([3.741, 7.5807, 142.0684, 34.9849, 0.4112, 0.0539])
>>> row[0].pValues
DenseVector([0.1928, 0.1105, 0.007, 0.0274, 0.5871, 0.838])
>>> fValueResult = FValueTest.test(dataset, 'features', 'label', True)
>>> row = fValueResult.orderBy("featureIndex").collect()
>>> row[0].fValue
3.7409548308350593
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.FValueTest
args = [
_py2java(sc, arg)
for arg in (dataset, featuresCol, labelCol, flatten)
]
return _java2py(sc, javaTestObj.test(*args))
def _transfer_param_map_from_java(self, javaParamMap):
"""
Transforms a Java ParamMap into a Python ParamMap.
"""
sc = SparkContext._active_spark_context
paramMap = dict()
for pair in javaParamMap.toList():
param = pair.param()
if self.hasParam(str(param.name())):
paramMap[self.getParam(param.name())] = _java2py(sc, pair.value())
return paramMap
def test(dataset, featuresCol, labelCol, flatten=False):
"""
Perform an ANOVA test using dataset.
:param dataset:
DataFrame of categorical labels and continuous features.
:param featuresCol:
Name of features column in dataset, of type `Vector` (`VectorUDT`).
:param labelCol:
Name of label column in dataset, of any numerical type.
:param flatten: if True, flattens the returned dataframe.
:return:
DataFrame containing the test result for every feature against the label.
If flatten is True, this DataFrame will contain one row per feature with the following
fields:
- `featureIndex: int`
- `pValue: float`
- `degreesOfFreedom: int`
- `fValue: float`
If flatten is False, this DataFrame will contain a single Row with the following fields:
- `pValues: Vector`
- `degreesOfFreedom: Array[int]`
- `fValues: Vector`
Each of these fields has one value per feature.
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.stat import ANOVATest
>>> dataset = [[2.0, Vectors.dense([0.43486404, 0.57153633, 0.43175686,
... 0.51418671, 0.61632374, 0.96565515])],
... [1.0, Vectors.dense([0.49162732, 0.6785187, 0.85460572,
... 0.59784822, 0.12394819, 0.53783355])],
... [2.0, Vectors.dense([0.30879653, 0.54904515, 0.17103889,
... 0.40492506, 0.18957493, 0.5440016])],
... [3.0, Vectors.dense([0.68114391, 0.60549825, 0.69094651,
... 0.62102109, 0.05471483, 0.96449167])]]
>>> dataset = spark.createDataFrame(dataset, ["label", "features"])
>>> anovaResult = ANOVATest.test(dataset, 'features', 'label')
>>> row = anovaResult.select("fValues", "pValues").collect()
>>> row[0].fValues
DenseVector([4.0264, 18.4713, 3.4659, 1.9042, 0.5532, 0.512])
>>> row[0].pValues
DenseVector([0.3324, 0.1623, 0.3551, 0.456, 0.689, 0.7029])
>>> anovaResult = ANOVATest.test(dataset, 'features', 'label', True)
>>> row = anovaResult.orderBy("featureIndex").collect()
>>> row[0].fValue
4.026438671875297
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.ANOVATest
args = [
_py2java(sc, arg)
for arg in (dataset, featuresCol, labelCol, flatten)
]
return _java2py(sc, javaTestObj.test(*args))
def corr(dataset: DataFrame,
column: str,
method: str = "pearson") -> DataFrame:
"""
Compute the correlation matrix with specified method using dataset.
.. versionadded:: 2.2.0
Parameters
----------
dataset : :py:class:`pyspark.sql.DataFrame`
A DataFrame.
column : str
The name of the column of vectors for which the correlation coefficient needs
to be computed. This must be a column of the dataset, and it must contain
Vector objects.
method : str, optional
String specifying the method to use for computing correlation.
Supported: `pearson` (default), `spearman`.
Returns
-------
A DataFrame that contains the correlation matrix of the column of vectors. This
DataFrame contains a single row and a single column of name `METHODNAME(COLUMN)`.
Examples
--------
>>> from pyspark.ml.linalg import DenseMatrix, Vectors
>>> from pyspark.ml.stat import Correlation
>>> dataset = [[Vectors.dense([1, 0, 0, -2])],
... [Vectors.dense([4, 5, 0, 3])],
... [Vectors.dense([6, 7, 0, 8])],
... [Vectors.dense([9, 0, 0, 1])]]
>>> dataset = spark.createDataFrame(dataset, ['features'])
>>> pearsonCorr = Correlation.corr(dataset, 'features', 'pearson').collect()[0][0]
>>> print(str(pearsonCorr).replace('nan', 'NaN'))
DenseMatrix([[ 1. , 0.0556..., NaN, 0.4004...],
[ 0.0556..., 1. , NaN, 0.9135...],
[ NaN, NaN, 1. , NaN],
[ 0.4004..., 0.9135..., NaN, 1. ]])
>>> spearmanCorr = Correlation.corr(dataset, 'features', method='spearman').collect()[0][0]
>>> print(str(spearmanCorr).replace('nan', 'NaN'))
DenseMatrix([[ 1. , 0.1054..., NaN, 0.4 ],
[ 0.1054..., 1. , NaN, 0.9486... ],
[ NaN, NaN, 1. , NaN],
[ 0.4 , 0.9486... , NaN, 1. ]])
"""
sc = SparkContext._active_spark_context
assert sc is not None
javaCorrObj = _jvm().org.apache.spark.ml.stat.Correlation
args = [_py2java(sc, arg) for arg in (dataset, column, method)]
return _java2py(sc, javaCorrObj.corr(*args))
def test(dataset, featuresCol, labelCol, flatten=False):
"""
Perform a Pearson's independence test using dataset.
:param dataset:
DataFrame of categorical labels and categorical features.
Real-valued features will be treated as categorical for each distinct value.
:param featuresCol:
Name of features column in dataset, of type `Vector` (`VectorUDT`).
:param labelCol:
Name of label column in dataset, of any numerical type.
:param flatten: if True, flattens the returned dataframe.
:return:
DataFrame containing the test result for every feature against the label.
If flatten is True, this DataFrame will contain one row per feature with the following
fields:
- `featureIndex: int`
- `pValue: float`
- `degreesOfFreedom: int`
- `statistic: float`
If flatten is False, this DataFrame will contain a single Row with the following fields:
- `pValues: Vector`
- `degreesOfFreedom: Array[int]`
- `statistics: Vector`
Each of these fields has one value per feature.
.. versionchanged:: 3.1.0
Added optional ``flatten`` argument.
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.stat import ChiSquareTest
>>> dataset = [[0, Vectors.dense([0, 0, 1])],
... [0, Vectors.dense([1, 0, 1])],
... [1, Vectors.dense([2, 1, 1])],
... [1, Vectors.dense([3, 1, 1])]]
>>> dataset = spark.createDataFrame(dataset, ["label", "features"])
>>> chiSqResult = ChiSquareTest.test(dataset, 'features', 'label')
>>> chiSqResult.select("degreesOfFreedom").collect()[0]
Row(degreesOfFreedom=[3, 1, 0])
>>> chiSqResult = ChiSquareTest.test(dataset, 'features', 'label', True)
>>> row = chiSqResult.orderBy("featureIndex").collect()
>>> row[0].statistic
4.0
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.ChiSquareTest
args = [
_py2java(sc, arg)
for arg in (dataset, featuresCol, labelCol, flatten)
]
return _java2py(sc, javaTestObj.test(*args))
def _transfer_params_from_java(self) -> None:
"""
Transforms the embedded params from the companion Java object.
"""
sc = SparkContext._active_spark_context
assert sc is not None and self._java_obj is not None
for param in self.params:
if self._java_obj.hasParam(param.name):
java_param = self._java_obj.getParam(param.name)
# SPARK-14931: Only check set params back to avoid default params mismatch.
if self._java_obj.isSet(java_param):
java_value = self._java_obj.getOrDefault(java_param)
if param.typeConverter.__name__.startswith("toList"):
value = [_java2py(sc, x) for x in list(java_value)]
else:
value = _java2py(sc, java_value)
self._set(**{param.name: value})
# SPARK-10931: Temporary fix for params that have a default in Java
if self._java_obj.hasDefault(
java_param) and not self.isDefined(param):
value = _java2py(
sc, self._java_obj.getDefault(java_param)).get()
self._setDefault(**{param.name: value})
def test(dataset, sampleCol, distName, *params):
"""
Conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution
equality. Currently supports the normal distribution, taking as parameters the mean and
standard deviation.
:param dataset:
a Dataset or a DataFrame containing the sample of data to test.
:param sampleCol:
Name of sample column in dataset, of any numerical type.
:param distName:
a `string` name for a theoretical distribution, currently only support "norm".
:param params:
a list of `Double` values specifying the parameters to be used for the theoretical
distribution. For "norm" distribution, the parameters includes mean and variance.
:return:
A DataFrame that contains the Kolmogorov-Smirnov test result for the input sampled data.
This DataFrame will contain a single Row with the following fields:
- `pValue: Double`
- `statistic: Double`
>>> from pyspark.ml.stat import KolmogorovSmirnovTest
>>> dataset = [[-1.0], [0.0], [1.0]]
>>> dataset = spark.createDataFrame(dataset, ['sample'])
>>> ksResult = KolmogorovSmirnovTest.test(dataset, 'sample', 'norm', 0.0, 1.0).first()
>>> round(ksResult.pValue, 3)
1.0
>>> round(ksResult.statistic, 3)
0.175
>>> dataset = [[2.0], [3.0], [4.0]]
>>> dataset = spark.createDataFrame(dataset, ['sample'])
>>> ksResult = KolmogorovSmirnovTest.test(dataset, 'sample', 'norm', 3.0, 1.0).first()
>>> round(ksResult.pValue, 3)
1.0
>>> round(ksResult.statistic, 3)
0.175
"""
sc = SparkContext._active_spark_context
javaTestObj = _jvm().org.apache.spark.ml.stat.KolmogorovSmirnovTest
dataset = _py2java(sc, dataset)
params = [float(param) for param in params]
return _java2py(sc, javaTestObj.test(dataset, sampleCol, distName,
_jvm().PythonUtils.toSeq(params)))
def _transfer_param_map_from_java(self, javaParamMap):
"""
Transforms a Java ParamMap into a Python ParamMap.
"""
sc = SparkContext._active_spark_context
paramMap = dict()
for pair in javaParamMap.toList():
param = pair.param()
if self.hasParam(str(param.name())):
java_obj = pair.value()
if sc._jvm.Class.forName("org.apache.spark.ml.PipelineStage").isInstance(java_obj):
# Note: JavaParams._from_java support both JavaEstimator/JavaTransformer class
# and Estimator/Transformer class which implements `_from_java` static method
# (such as OneVsRest, Pipeline class).
py_obj = JavaParams._from_java(java_obj)
else:
py_obj = _java2py(sc, java_obj)
paramMap[self.getParam(param.name())] = py_obj
return paramMap
def _transfer_params_from_java(self):
"""
Transforms the embedded params from the companion Java object.
"""
sc = SparkContext._active_spark_context
for param in self.params:
if self._java_obj.hasParam(param.name):
java_param = self._java_obj.getParam(param.name)
# SPARK-14931: Only check set params back to avoid default params mismatch.
complex_param_class = sc._gateway.jvm.org.apache.spark.ml.param.ComplexParam._java_lang_class
is_complex_param = complex_param_class.isAssignableFrom(
java_param.getClass())
if self._java_obj.isSet(java_param):
if is_complex_param:
value = self._java_obj.getOrDefault(java_param)
else:
value = _java2py(
sc, self._java_obj.getOrDefault(java_param))
self._set(**{param.name: value})
def corr(dataset, column, method="pearson"):
"""
Compute the correlation matrix with specified method using dataset.
:param dataset:
A Dataset or a DataFrame.
:param column:
The name of the column of vectors for which the correlation coefficient needs
to be computed. This must be a column of the dataset, and it must contain
Vector objects.
:param method:
String specifying the method to use for computing correlation.
Supported: `pearson` (default), `spearman`.
:return:
A DataFrame that contains the correlation matrix of the column of vectors. This
DataFrame contains a single row and a single column of name
'$METHODNAME($COLUMN)'.
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.stat import Correlation
>>> dataset = [[Vectors.dense([1, 0, 0, -2])],
... [Vectors.dense([4, 5, 0, 3])],
... [Vectors.dense([6, 7, 0, 8])],
... [Vectors.dense([9, 0, 0, 1])]]
>>> dataset = spark.createDataFrame(dataset, ['features'])
>>> pearsonCorr = Correlation.corr(dataset, 'features', 'pearson').collect()[0][0]
>>> print(str(pearsonCorr).replace('nan', 'NaN'))
DenseMatrix([[ 1. , 0.0556..., NaN, 0.4004...],
[ 0.0556..., 1. , NaN, 0.9135...],
[ NaN, NaN, 1. , NaN],
[ 0.4004..., 0.9135..., NaN, 1. ]])
>>> spearmanCorr = Correlation.corr(dataset, 'features', method='spearman').collect()[0][0]
>>> print(str(spearmanCorr).replace('nan', 'NaN'))
DenseMatrix([[ 1. , 0.1054..., NaN, 0.4 ],
[ 0.1054..., 1. , NaN, 0.9486... ],
[ NaN, NaN, 1. , NaN],
[ 0.4 , 0.9486... , NaN, 1. ]])
"""
sc = SparkContext._active_spark_context
javaCorrObj = _jvm().org.apache.spark.ml.stat.Correlation
args = [_py2java(sc, arg) for arg in (dataset, column, method)]
return _java2py(sc, javaCorrObj.corr(*args))
def corr(dataset, column, method="pearson"):
"""
Compute the correlation matrix with specified method using dataset.
:param dataset:
A Dataset or a DataFrame.
:param column:
The name of the column of vectors for which the correlation coefficient needs
to be computed. This must be a column of the dataset, and it must contain
Vector objects.
:param method:
String specifying the method to use for computing correlation.
Supported: `pearson` (default), `spearman`.
:return:
A DataFrame that contains the correlation matrix of the column of vectors. This
DataFrame contains a single row and a single column of name
'$METHODNAME($COLUMN)'.
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.stat import Correlation
>>> dataset = [[Vectors.dense([1, 0, 0, -2])],
... [Vectors.dense([4, 5, 0, 3])],
... [Vectors.dense([6, 7, 0, 8])],
... [Vectors.dense([9, 0, 0, 1])]]
>>> dataset = spark.createDataFrame(dataset, ['features'])
>>> pearsonCorr = Correlation.corr(dataset, 'features', 'pearson').collect()[0][0]
>>> print(str(pearsonCorr).replace('nan', 'NaN'))
DenseMatrix([[ 1. , 0.0556..., NaN, 0.4004...],
[ 0.0556..., 1. , NaN, 0.9135...],
[ NaN, NaN, 1. , NaN],
[ 0.4004..., 0.9135..., NaN, 1. ]])
>>> spearmanCorr = Correlation.corr(dataset, 'features', method='spearman').collect()[0][0]
>>> print(str(spearmanCorr).replace('nan', 'NaN'))
DenseMatrix([[ 1. , 0.1054..., NaN, 0.4 ],
[ 0.1054..., 1. , NaN, 0.9486... ],
[ NaN, NaN, 1. , NaN],
[ 0.4 , 0.9486... , NaN, 1. ]])
"""
sc = SparkContext._active_spark_context
javaCorrObj = _jvm().org.apache.spark.ml.stat.Correlation
args = [_py2java(sc, arg) for arg in (dataset, column, method)]
return _java2py(sc, javaCorrObj.corr(*args))
def _from_java(cls, java_stage):
"""
Given a Java CrossValidatorModel, create and return a Python wrapper of it.
Used for ML persistence.
"""
sc = SparkContext._active_spark_context
bestModel = JavaParams._from_java(java_stage.bestModel())
avgMetrics = _java2py(sc, java_stage.avgMetrics())
estimator, epms, evaluator = super(CrossValidatorModel, cls)._from_java_impl(java_stage)
py_stage = cls(bestModel=bestModel, avgMetrics=avgMetrics).setEstimator(estimator)
py_stage = py_stage.setEstimatorParamMaps(epms).setEvaluator(evaluator)
if java_stage.hasSubModels():
py_stage.subModels = [[JavaParams._from_java(sub_model)
for sub_model in fold_sub_models]
for fold_sub_models in java_stage.subModels()]
py_stage._resetUid(java_stage.uid())
return py_stage
def _from_java(cls, java_object):
# primitives and spark data types are converted automatically by
# _java2py(), in those cases there is nothing to do
if type(java_object) != py4j.java_gateway.JavaObject:
return java_object
# construct a mapping of our python wrapped classes to
# java/scala classes
wrapped_classes = {}
for cls in SageMakerJavaWrapper.__subclasses__():
wrapped_classes[cls._wrapped_class] = cls
class_name = java_object.getClass().getName()
# SageMakerJavaWrapper classes know how to convert themselves from a Java Object
# otherwise hand over to _java2py and hope for the best.
if class_name in wrapped_classes:
return wrapped_classes[class_name]._from_java(java_object)
elif class_name.startswith("scala.None"):
return None
else:
sc = SparkContext._active_spark_context
return _java2py(sc, java_object)
from pyspark.sql import SparkSession
from pyspark.ml.common import _java2py
from Chapter01.utilities01_py.helper_python import create_session
# ~/spark-2.4.6-bin-hadoop2.7/bin/spark-submit --driver-class-path ~/IdeaProjects/The-Spark-Workshop/target/packt-uber-jar.jar ~/IdeaProjects/The-Spark-Workshop/Chapter04/Exercise4_06/Exercise4_06.py
if __name__ == "__main__":
session: SparkSession = create_session(2, "PySpark <> JVM")
session.sparkContext.setLogLevel('ERROR')
python_rdd = session.sparkContext.range(0, 5)
java_rdd = session.sparkContext._jvm.SerDe.pythonToJava(
python_rdd._jrdd, True)
mapped_java_rdd = session.sparkContext._jvm.Exercise4_06.ScalaObject.executeInScala(
java_rdd)
mapped_python_rdd = _java2py(session.sparkContext, mapped_java_rdd)
print(mapped_python_rdd.collect())
def _call_java(self, name, *args):
m = getattr(self._java_obj, name)
sc = SparkContext._active_spark_context
java_args = [_py2java(sc, arg) for arg in args]
return _java2py(sc, m(*java_args))
def _call_java(self, name, *args):
m = getattr(self._java_obj, name)
sc = SparkContext._active_spark_context
java_args = [_py2java(sc, arg) for arg in args]
return _java2py(sc, m(*java_args))
from sys import argv
from pyspark.ml.common import _java2py
from Chapter01.utilities01_py.helper_python import create_session
from Chapter02.utilities02_py.helper_python import sample_warc_loc, extract_raw_records, parse_raw_warc
# ~/spark-2.4.6-bin-hadoop2.7/bin/spark-submit --driver-class-path ~/IdeaProjects/The-Spark-Workshop/target/packt-uber-jar.jar:/Users/a/.m2/repository/com/google/guava/guava/28.2-jre/guava-28.2-jre.jar:/Users/a/.m2/repository/org/apache/commons/commons-compress/1.20/commons-compress-1.20.jar ~/IdeaProjects/The-Spark-Workshop/Chapter04/Activity4_03/Activity4_03.py ~/Output_Act4_3
if __name__ == "__main__":
output_dir = argv[1]
session = create_session(3, 'WARC Parser')
warc_records = extract_raw_records(sample_warc_loc, session) \
.flatMap(lambda record: parse_raw_warc(record)) \
.filter(lambda record: record.warc_type == 'response')
plaintexts_rdd = warc_records.map(lambda record: record.html_source)
java_rdd = session.sparkContext._jvm.SerDe.pythonToJava(
plaintexts_rdd._jrdd, True)
tagged_java_rdd = session.sparkContext._jvm.Activity4_03.Activity4_03.tagJavaRDD(
java_rdd)
tagged_python_rdd = _java2py(session.sparkContext, tagged_java_rdd)
tagged_python_rdd.saveAsTextFile(output_dir)
def feature_aggregated_shap(self, input_cols):
return _java2py(self._sc,
self._shapley_model.getShapValuesFromModel(input_cols))
def calculate(self):
return _java2py(self._sc, self._shapley_model.calculate())
