Source code for synapse.ml.causal.OrthoForestDMLModel

# 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.platform import running_on_synapse_internal
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 OrthoForestDMLModel(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaModel): """ Args: confidenceLevel (float): confidence level, default value is 0.975 confounderVecCol (str): Confounders to control for featuresCol (str): The name of the features column forest (object): Forest Trees produced in Ortho Forest DML Estimator heterogeneityVecCol (str): Vector to divide the treatment by maxDepth (int): Max Depth of Tree maxIter (int): maximum number of iterations (>= 0) minSamplesLeaf (int): Max Depth of Tree numTrees (int): Number of trees outcomeCol (str): outcome column outcomeModel (object): outcome model to run outcomeResidualCol (str): Outcome Residual Column outputCol (str): The name of the output column outputHighCol (str): Output Confidence Interval Low outputLowCol (str): Output Confidence Interval Low parallelism (int): the number of threads to use when running parallel algorithms sampleSplitRatio (list): Sample split ratio for cross-fitting. Default: [0.5, 0.5]. treatmentCol (str): treatment column treatmentModel (object): treatment model to run treatmentResidualCol (str): Treatment Residual Column weightCol (str): The name of the weight column """ confidenceLevel = Param(Params._dummy(), "confidenceLevel", "confidence level, default value is 0.975", typeConverter=TypeConverters.toFloat) confounderVecCol = Param(Params._dummy(), "confounderVecCol", "Confounders to control for", typeConverter=TypeConverters.toString) featuresCol = Param(Params._dummy(), "featuresCol", "The name of the features column", typeConverter=TypeConverters.toString) forest = Param(Params._dummy(), "forest", "Forest Trees produced in Ortho Forest DML Estimator") heterogeneityVecCol = Param(Params._dummy(), "heterogeneityVecCol", "Vector to divide the treatment by", typeConverter=TypeConverters.toString) maxDepth = Param(Params._dummy(), "maxDepth", "Max Depth of Tree", typeConverter=TypeConverters.toInt) maxIter = Param(Params._dummy(), "maxIter", "maximum number of iterations (>= 0)", typeConverter=TypeConverters.toInt) minSamplesLeaf = Param(Params._dummy(), "minSamplesLeaf", "Max Depth of Tree", typeConverter=TypeConverters.toInt) numTrees = Param(Params._dummy(), "numTrees", "Number of trees", typeConverter=TypeConverters.toInt) outcomeCol = Param(Params._dummy(), "outcomeCol", "outcome column", typeConverter=TypeConverters.toString) outcomeModel = Param(Params._dummy(), "outcomeModel", "outcome model to run") outcomeResidualCol = Param(Params._dummy(), "outcomeResidualCol", "Outcome Residual Column", typeConverter=TypeConverters.toString) outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString) outputHighCol = Param(Params._dummy(), "outputHighCol", "Output Confidence Interval Low", typeConverter=TypeConverters.toString) outputLowCol = Param(Params._dummy(), "outputLowCol", "Output Confidence Interval Low", typeConverter=TypeConverters.toString) parallelism = Param(Params._dummy(), "parallelism", "the number of threads to use when running parallel algorithms", typeConverter=TypeConverters.toInt) sampleSplitRatio = Param(Params._dummy(), "sampleSplitRatio", "Sample split ratio for cross-fitting. Default: [0.5, 0.5].", typeConverter=TypeConverters.toListFloat) treatmentCol = Param(Params._dummy(), "treatmentCol", "treatment column", typeConverter=TypeConverters.toString) treatmentModel = Param(Params._dummy(), "treatmentModel", "treatment model to run") treatmentResidualCol = Param(Params._dummy(), "treatmentResidualCol", "Treatment Residual Column", typeConverter=TypeConverters.toString) weightCol = Param(Params._dummy(), "weightCol", "The name of the weight column", typeConverter=TypeConverters.toString) @keyword_only def __init__( self, java_obj=None, confidenceLevel=0.975, confounderVecCol="XW", featuresCol=None, forest=None, heterogeneityVecCol="X", maxDepth=5, maxIter=1, minSamplesLeaf=10, numTrees=20, outcomeCol=None, outcomeModel=None, outcomeResidualCol="OutcomeResidual", outputCol="EffectAverage", outputHighCol="EffectUpperBound", outputLowCol="EffectLowerBound", parallelism=10, sampleSplitRatio=[0.5,0.5], treatmentCol=None, treatmentModel=None, treatmentResidualCol="TreatmentResidual", weightCol=None ): super(OrthoForestDMLModel, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.causal.OrthoForestDMLModel", self.uid) else: self._java_obj = java_obj self._setDefault(confidenceLevel=0.975) self._setDefault(confounderVecCol="XW") self._setDefault(heterogeneityVecCol="X") self._setDefault(maxDepth=5) self._setDefault(maxIter=1) self._setDefault(minSamplesLeaf=10) self._setDefault(numTrees=20) self._setDefault(outcomeResidualCol="OutcomeResidual") self._setDefault(outputCol="EffectAverage") self._setDefault(outputHighCol="EffectUpperBound") self._setDefault(outputLowCol="EffectLowerBound") self._setDefault(parallelism=10) self._setDefault(sampleSplitRatio=[0.5,0.5]) self._setDefault(treatmentResidualCol="TreatmentResidual") 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, confidenceLevel=0.975, confounderVecCol="XW", featuresCol=None, forest=None, heterogeneityVecCol="X", maxDepth=5, maxIter=1, minSamplesLeaf=10, numTrees=20, outcomeCol=None, outcomeModel=None, outcomeResidualCol="OutcomeResidual", outputCol="EffectAverage", outputHighCol="EffectUpperBound", outputLowCol="EffectLowerBound", parallelism=10, sampleSplitRatio=[0.5,0.5], treatmentCol=None, treatmentModel=None, treatmentResidualCol="TreatmentResidual", weightCol=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.causal.OrthoForestDMLModel"
@staticmethod def _from_java(java_stage): module_name=OrthoForestDMLModel.__module__ module_name=module_name.rsplit(".", 1)[0] + ".OrthoForestDMLModel" return from_java(java_stage, module_name)
[docs] def setConfidenceLevel(self, value): """ Args: confidenceLevel: confidence level, default value is 0.975 """ self._set(confidenceLevel=value) return self
[docs] def setConfounderVecCol(self, value): """ Args: confounderVecCol: Confounders to control for """ self._set(confounderVecCol=value) return self
[docs] def setFeaturesCol(self, value): """ Args: featuresCol: The name of the features column """ self._set(featuresCol=value) return self
[docs] def setForest(self, value): """ Args: forest: Forest Trees produced in Ortho Forest DML Estimator """ self._set(forest=value) return self
[docs] def setHeterogeneityVecCol(self, value): """ Args: heterogeneityVecCol: Vector to divide the treatment by """ self._set(heterogeneityVecCol=value) return self
[docs] def setMaxDepth(self, value): """ Args: maxDepth: Max Depth of Tree """ self._set(maxDepth=value) return self
[docs] def setMaxIter(self, value): """ Args: maxIter: maximum number of iterations (>= 0) """ self._set(maxIter=value) return self
[docs] def setMinSamplesLeaf(self, value): """ Args: minSamplesLeaf: Max Depth of Tree """ self._set(minSamplesLeaf=value) return self
[docs] def setNumTrees(self, value): """ Args: numTrees: Number of trees """ self._set(numTrees=value) return self
[docs] def setOutcomeCol(self, value): """ Args: outcomeCol: outcome column """ self._set(outcomeCol=value) return self
[docs] def setOutcomeModel(self, value): """ Args: outcomeModel: outcome model to run """ self._set(outcomeModel=value) return self
[docs] def setOutcomeResidualCol(self, value): """ Args: outcomeResidualCol: Outcome Residual Column """ self._set(outcomeResidualCol=value) return self
[docs] def setOutputCol(self, value): """ Args: outputCol: The name of the output column """ self._set(outputCol=value) return self
[docs] def setOutputHighCol(self, value): """ Args: outputHighCol: Output Confidence Interval Low """ self._set(outputHighCol=value) return self
[docs] def setOutputLowCol(self, value): """ Args: outputLowCol: Output Confidence Interval Low """ self._set(outputLowCol=value) return self
[docs] def setParallelism(self, value): """ Args: parallelism: the number of threads to use when running parallel algorithms """ self._set(parallelism=value) return self
[docs] def setSampleSplitRatio(self, value): """ Args: sampleSplitRatio: Sample split ratio for cross-fitting. Default: [0.5, 0.5]. """ self._set(sampleSplitRatio=value) return self
[docs] def setTreatmentCol(self, value): """ Args: treatmentCol: treatment column """ self._set(treatmentCol=value) return self
[docs] def setTreatmentModel(self, value): """ Args: treatmentModel: treatment model to run """ self._set(treatmentModel=value) return self
[docs] def setTreatmentResidualCol(self, value): """ Args: treatmentResidualCol: Treatment Residual Column """ self._set(treatmentResidualCol=value) return self
[docs] def setWeightCol(self, value): """ Args: weightCol: The name of the weight column """ self._set(weightCol=value) return self
[docs] def getConfidenceLevel(self): """ Returns: confidenceLevel: confidence level, default value is 0.975 """ return self.getOrDefault(self.confidenceLevel)
[docs] def getConfounderVecCol(self): """ Returns: confounderVecCol: Confounders to control for """ return self.getOrDefault(self.confounderVecCol)
[docs] def getFeaturesCol(self): """ Returns: featuresCol: The name of the features column """ return self.getOrDefault(self.featuresCol)
[docs] def getForest(self): """ Returns: forest: Forest Trees produced in Ortho Forest DML Estimator """ return self.getOrDefault(self.forest)
[docs] def getHeterogeneityVecCol(self): """ Returns: heterogeneityVecCol: Vector to divide the treatment by """ return self.getOrDefault(self.heterogeneityVecCol)
[docs] def getMaxDepth(self): """ Returns: maxDepth: Max Depth of Tree """ return self.getOrDefault(self.maxDepth)
[docs] def getMaxIter(self): """ Returns: maxIter: maximum number of iterations (>= 0) """ return self.getOrDefault(self.maxIter)
[docs] def getMinSamplesLeaf(self): """ Returns: minSamplesLeaf: Max Depth of Tree """ return self.getOrDefault(self.minSamplesLeaf)
[docs] def getNumTrees(self): """ Returns: numTrees: Number of trees """ return self.getOrDefault(self.numTrees)
[docs] def getOutcomeCol(self): """ Returns: outcomeCol: outcome column """ return self.getOrDefault(self.outcomeCol)
[docs] def getOutcomeModel(self): """ Returns: outcomeModel: outcome model to run """ return JavaParams._from_java(self._java_obj.getOutcomeModel())
[docs] def getOutcomeResidualCol(self): """ Returns: outcomeResidualCol: Outcome Residual Column """ return self.getOrDefault(self.outcomeResidualCol)
[docs] def getOutputCol(self): """ Returns: outputCol: The name of the output column """ return self.getOrDefault(self.outputCol)
[docs] def getOutputHighCol(self): """ Returns: outputHighCol: Output Confidence Interval Low """ return self.getOrDefault(self.outputHighCol)
[docs] def getOutputLowCol(self): """ Returns: outputLowCol: Output Confidence Interval Low """ return self.getOrDefault(self.outputLowCol)
[docs] def getParallelism(self): """ Returns: parallelism: the number of threads to use when running parallel algorithms """ return self.getOrDefault(self.parallelism)
[docs] def getSampleSplitRatio(self): """ Returns: sampleSplitRatio: Sample split ratio for cross-fitting. Default: [0.5, 0.5]. """ return self.getOrDefault(self.sampleSplitRatio)
[docs] def getTreatmentCol(self): """ Returns: treatmentCol: treatment column """ return self.getOrDefault(self.treatmentCol)
[docs] def getTreatmentModel(self): """ Returns: treatmentModel: treatment model to run """ return JavaParams._from_java(self._java_obj.getTreatmentModel())
[docs] def getTreatmentResidualCol(self): """ Returns: treatmentResidualCol: Treatment Residual Column """ return self.getOrDefault(self.treatmentResidualCol)
[docs] def getWeightCol(self): """ Returns: weightCol: The name of the weight column """ return self.getOrDefault(self.weightCol)