# 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 import SparkContext, SQLContext
from pyspark.sql import DataFrame
from pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from synapse.ml.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.evaluation import JavaEvaluator
from pyspark.ml.common import inherit_doc
from synapse.ml.core.schema.Utils import *
from pyspark.ml.param import TypeConverters
from synapse.ml.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
[docs]@inherit_doc
class TextLIME(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaModel):
"""
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
outputCol (str): The name of the output column
predictionCol (str): prediction column name
regularization (float): regularization param for the lasso
samplingFraction (float): The fraction of superpixels to keep on
tokenCol (str): The column holding the token
"""
inputCol = Param(Params._dummy(), "inputCol", "The name of the input column", typeConverter=TypeConverters.toString)
model = Param(Params._dummy(), "model", "Model to try to locally approximate")
nSamples = Param(Params._dummy(), "nSamples", "The number of samples to generate", typeConverter=TypeConverters.toInt)
outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString)
predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name", typeConverter=TypeConverters.toString)
regularization = Param(Params._dummy(), "regularization", "regularization param for the lasso", typeConverter=TypeConverters.toFloat)
samplingFraction = Param(Params._dummy(), "samplingFraction", "The fraction of superpixels to keep on", typeConverter=TypeConverters.toFloat)
tokenCol = Param(Params._dummy(), "tokenCol", "The column holding the token", typeConverter=TypeConverters.toString)
@keyword_only
def __init__(
self,
java_obj=None,
inputCol=None,
model=None,
nSamples=1000,
outputCol=None,
predictionCol="prediction",
regularization=0.0,
samplingFraction=0.3,
tokenCol=None
):
super(TextLIME, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.lime.TextLIME", self.uid)
else:
self._java_obj = java_obj
self._setDefault(nSamples=1000)
self._setDefault(predictionCol="prediction")
self._setDefault(regularization=0.0)
self._setDefault(samplingFraction=0.3)
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
if java_obj is None:
for k,v in kwargs.items():
if v is not None:
getattr(self, "set" + k[0].upper() + k[1:])(v)
[docs] @keyword_only
def setParams(
self,
inputCol=None,
model=None,
nSamples=1000,
outputCol=None,
predictionCol="prediction",
regularization=0.0,
samplingFraction=0.3,
tokenCol=None
):
"""
Set the (keyword only) parameters
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[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.azure.synapse.ml.lime.TextLIME"
@staticmethod
def _from_java(java_stage):
module_name=TextLIME.__module__
module_name=module_name.rsplit(".", 1)[0] + ".TextLIME"
return from_java(java_stage, module_name)
[docs] def setInputCol(self, value):
"""
Args:
inputCol: The name of the input column
"""
self._set(inputCol=value)
return self
[docs] def setModel(self, value):
"""
Args:
model: Model to try to locally approximate
"""
self._set(model=value)
return self
[docs] def setNSamples(self, value):
"""
Args:
nSamples: The number of samples to generate
"""
self._set(nSamples=value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def setPredictionCol(self, value):
"""
Args:
predictionCol: prediction column name
"""
self._set(predictionCol=value)
return self
[docs] def setRegularization(self, value):
"""
Args:
regularization: regularization param for the lasso
"""
self._set(regularization=value)
return self
[docs] def setSamplingFraction(self, value):
"""
Args:
samplingFraction: The fraction of superpixels to keep on
"""
self._set(samplingFraction=value)
return self
[docs] def setTokenCol(self, value):
"""
Args:
tokenCol: The column holding the token
"""
self._set(tokenCol=value)
return self
[docs] def getInputCol(self):
"""
Returns:
inputCol: The name of the input column
"""
return self.getOrDefault(self.inputCol)
[docs] def getModel(self):
"""
Returns:
model: Model to try to locally approximate
"""
return JavaParams._from_java(self._java_obj.getModel())
[docs] def getNSamples(self):
"""
Returns:
nSamples: The number of samples to generate
"""
return self.getOrDefault(self.nSamples)
[docs] def getOutputCol(self):
"""
Returns:
outputCol: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def getPredictionCol(self):
"""
Returns:
predictionCol: prediction column name
"""
return self.getOrDefault(self.predictionCol)
[docs] def getRegularization(self):
"""
Returns:
regularization: regularization param for the lasso
"""
return self.getOrDefault(self.regularization)
[docs] def getSamplingFraction(self):
"""
Returns:
samplingFraction: The fraction of superpixels to keep on
"""
return self.getOrDefault(self.samplingFraction)
[docs] def getTokenCol(self):
"""
Returns:
tokenCol: The column holding the token
"""
return self.getOrDefault(self.tokenCol)