# 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 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 TabularLIMEModel(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
columnMeans (list): the means of each of the columns for perturbation
columnSTDs (list): the standard deviations of each of the columns for perturbation
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 (default: prediction)
regularization (double): regularization param for the lasso
samplingFraction (double): The fraction of superpixels to keep on
"""
@keyword_only
def __init__(self, columnMeans=None, columnSTDs=None, inputCol=None, model=None, nSamples=None, outputCol=None, predictionCol="prediction", regularization=None, samplingFraction=None):
super(TabularLIMEModel, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.lime.TabularLIMEModel")
self._cache = {}
self.columnMeans = Param(self, "columnMeans", "columnMeans: the means of each of the columns for perturbation")
self.columnSTDs = Param(self, "columnSTDs", "columnSTDs: the standard deviations of each of the columns for perturbation")
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")
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")
self.samplingFraction = Param(self, "samplingFraction", "samplingFraction: The fraction of superpixels to keep on")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, columnMeans=None, columnSTDs=None, inputCol=None, model=None, nSamples=None, outputCol=None, predictionCol="prediction", regularization=None, samplingFraction=None):
"""
Set the (keyword only) parameters
Args:
columnMeans (list): the means of each of the columns for perturbation
columnSTDs (list): the standard deviations of each of the columns for perturbation
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 (default: prediction)
regularization (double): regularization param for the lasso
samplingFraction (double): The fraction of superpixels to keep on
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def getColumnMeans(self):
"""
Returns:
list: the means of each of the columns for perturbation
"""
return self.getOrDefault(self.columnMeans)
[docs] def getColumnSTDs(self):
"""
Returns:
list: the standard deviations of each of the columns for perturbation
"""
return self.getOrDefault(self.columnSTDs)
[docs] def getModel(self):
"""
Returns:
object: Model to try to locally approximate
"""
return self._cache.get("model", None)
[docs] def getNSamples(self):
"""
Returns:
int: The number of samples to generate
"""
return self.getOrDefault(self.nSamples)
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def getPredictionCol(self):
"""
Returns:
str: prediction column name (default: prediction)
"""
return self.getOrDefault(self.predictionCol)
[docs] def getRegularization(self):
"""
Returns:
double: regularization param for the lasso
"""
return self.getOrDefault(self.regularization)
[docs] def getSamplingFraction(self):
"""
Returns:
double: The fraction of superpixels to keep on
"""
return self.getOrDefault(self.samplingFraction)
[docs] def setColumnMeans(self, value):
"""
Args:
columnMeans: the means of each of the columns for perturbation
"""
self._set(columnMeans=value)
return self
[docs] def setColumnSTDs(self, value):
"""
Args:
columnSTDs: the standard deviations of each of the columns for perturbation
"""
self._set(columnSTDs=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 (default: prediction)
"""
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] @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)