# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.
import sys
if sys.version >= '3':
basestring = str
from pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from mmlspark.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.common import inherit_doc
from mmlspark.core.schema.Utils import *
from mmlspark.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
[docs]@inherit_doc
class TabularLIME(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
inputCol (str): The name of the input column
model (object): Model to try to locally approximate
nSamples (int): The number of samples to generate (default: 1000)
outputCol (str): The name of the output column
predictionCol (str): prediction column name (default: prediction)
regularization (double): regularization param for the lasso (default: 0.0)
samplingFraction (double): The fraction of superpixels to keep on (default: 0.3)
"""
@keyword_only
def __init__(self, inputCol=None, model=None, nSamples=1000, outputCol=None, predictionCol="prediction", regularization=0.0, samplingFraction=0.3):
super(TabularLIME, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.lime.TabularLIME")
self._cache = {}
self.inputCol = Param(self, "inputCol", "inputCol: The name of the input column")
self.model = Param(self, "model", "model: Model to try to locally approximate", generateTypeConverter("model", self._cache, complexTypeConverter))
self.nSamples = Param(self, "nSamples", "nSamples: The number of samples to generate (default: 1000)")
self._setDefault(nSamples=1000)
self.outputCol = Param(self, "outputCol", "outputCol: The name of the output column")
self.predictionCol = Param(self, "predictionCol", "predictionCol: prediction column name (default: prediction)")
self._setDefault(predictionCol="prediction")
self.regularization = Param(self, "regularization", "regularization: regularization param for the lasso (default: 0.0)")
self._setDefault(regularization=0.0)
self.samplingFraction = Param(self, "samplingFraction", "samplingFraction: The fraction of superpixels to keep on (default: 0.3)")
self._setDefault(samplingFraction=0.3)
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, inputCol=None, model=None, nSamples=1000, outputCol=None, predictionCol="prediction", regularization=0.0, samplingFraction=0.3):
"""
Set the (keyword only) parameters
Args:
inputCol (str): The name of the input column
model (object): Model to try to locally approximate
nSamples (int): The number of samples to generate (default: 1000)
outputCol (str): The name of the output column
predictionCol (str): prediction column name (default: prediction)
regularization (double): regularization param for the lasso (default: 0.0)
samplingFraction (double): The fraction of superpixels to keep on (default: 0.3)
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def setModel(self, value):
"""
Args:
model (object): Model to try to locally approximate
"""
self._set(model=value)
return self
[docs] def getModel(self):
"""
Returns:
object: Model to try to locally approximate
"""
return self._cache.get("model", None)
[docs] def setNSamples(self, value):
"""
Args:
nSamples (int): The number of samples to generate (default: 1000)
"""
self._set(nSamples=value)
return self
[docs] def getNSamples(self):
"""
Returns:
int: The number of samples to generate (default: 1000)
"""
return self.getOrDefault(self.nSamples)
[docs] def setOutputCol(self, value):
"""
Args:
outputCol (str): The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def setPredictionCol(self, value):
"""
Args:
predictionCol (str): prediction column name (default: prediction)
"""
self._set(predictionCol=value)
return self
[docs] def getPredictionCol(self):
"""
Returns:
str: prediction column name (default: prediction)
"""
return self.getOrDefault(self.predictionCol)
[docs] def setRegularization(self, value):
"""
Args:
regularization (double): regularization param for the lasso (default: 0.0)
"""
self._set(regularization=value)
return self
[docs] def getRegularization(self):
"""
Returns:
double: regularization param for the lasso (default: 0.0)
"""
return self.getOrDefault(self.regularization)
[docs] def setSamplingFraction(self, value):
"""
Args:
samplingFraction (double): The fraction of superpixels to keep on (default: 0.3)
"""
self._set(samplingFraction=value)
return self
[docs] def getSamplingFraction(self):
"""
Returns:
double: The fraction of superpixels to keep on (default: 0.3)
"""
return self.getOrDefault(self.samplingFraction)
[docs] @classmethod
def read(cls):
""" Returns an MLReader instance for this class. """
return JavaMMLReader(cls)
[docs] @staticmethod
def getJavaPackage():
""" Returns package name String. """
return "com.microsoft.ml.spark.lime.TabularLIME"
@staticmethod
def _from_java(java_stage):
module_name=TabularLIME.__module__
module_name=module_name.rsplit(".", 1)[0] + ".TabularLIME"
return from_java(java_stage, module_name)
def _create_model(self, java_model):
return TabularLIMEModel(java_model)
[docs]class TabularLIMEModel(ComplexParamsMixin, JavaModel, JavaMLWritable, JavaMLReadable):
"""
Model fitted by :class:`TabularLIME`.
This class is left empty on purpose.
All necessary methods are exposed through inheritance.
"""
[docs] @classmethod
def read(cls):
""" Returns an MLReader instance for this class. """
return JavaMMLReader(cls)
[docs] @staticmethod
def getJavaPackage():
""" Returns package name String. """
return "com.microsoft.ml.spark.lime.TabularLIMEModel"
@staticmethod
def _from_java(java_stage):
module_name=TabularLIMEModel.__module__
module_name=module_name.rsplit(".", 1)[0] + ".TabularLIMEModel"
return from_java(java_stage, module_name)