Source code for synapse.ml.featurize.Featurize

# 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
from pyspark.ml import PipelineModel

[docs]@inherit_doc class Featurize(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator): """ Args: imputeMissing (bool): Whether to impute missing values inputCols (list): The names of the input columns numFeatures (int): Number of features to hash string columns to oneHotEncodeCategoricals (bool): One-hot encode categorical columns outputCol (object): The name of the output column """ imputeMissing = Param(Params._dummy(), "imputeMissing", "Whether to impute missing values", typeConverter=TypeConverters.toBoolean) inputCols = Param(Params._dummy(), "inputCols", "The names of the input columns", typeConverter=TypeConverters.toListString) numFeatures = Param(Params._dummy(), "numFeatures", "Number of features to hash string columns to", typeConverter=TypeConverters.toInt) oneHotEncodeCategoricals = Param(Params._dummy(), "oneHotEncodeCategoricals", "One-hot encode categorical columns", typeConverter=TypeConverters.toBoolean) outputCol = Param(Params._dummy(), "outputCol", "The name of the output column") @keyword_only def __init__( self, java_obj=None, imputeMissing=True, inputCols=None, numFeatures=262144, oneHotEncodeCategoricals=True, outputCol=None ): super(Featurize, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.featurize.Featurize", self.uid) else: self._java_obj = java_obj self._setDefault(imputeMissing=True) self._setDefault(numFeatures=262144) self._setDefault(oneHotEncodeCategoricals=True) 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, imputeMissing=True, inputCols=None, numFeatures=262144, oneHotEncodeCategoricals=True, outputCol=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.featurize.Featurize"
@staticmethod def _from_java(java_stage): module_name=Featurize.__module__ module_name=module_name.rsplit(".", 1)[0] + ".Featurize" return from_java(java_stage, module_name)
[docs] def setImputeMissing(self, value): """ Args: imputeMissing: Whether to impute missing values """ self._set(imputeMissing=value) return self
[docs] def setInputCols(self, value): """ Args: inputCols: The names of the input columns """ self._set(inputCols=value) return self
[docs] def setNumFeatures(self, value): """ Args: numFeatures: Number of features to hash string columns to """ self._set(numFeatures=value) return self
[docs] def setOneHotEncodeCategoricals(self, value): """ Args: oneHotEncodeCategoricals: One-hot encode categorical columns """ self._set(oneHotEncodeCategoricals=value) return self
[docs] def setOutputCol(self, value): """ Args: outputCol: The name of the output column """ self._set(outputCol=value) return self
[docs] def getImputeMissing(self): """ Returns: imputeMissing: Whether to impute missing values """ return self.getOrDefault(self.imputeMissing)
[docs] def getInputCols(self): """ Returns: inputCols: The names of the input columns """ return self.getOrDefault(self.inputCols)
[docs] def getNumFeatures(self): """ Returns: numFeatures: Number of features to hash string columns to """ return self.getOrDefault(self.numFeatures)
[docs] def getOneHotEncodeCategoricals(self): """ Returns: oneHotEncodeCategoricals: One-hot encode categorical columns """ return self.getOrDefault(self.oneHotEncodeCategoricals)
[docs] def getOutputCol(self): """ Returns: outputCol: The name of the output column """ return self.getOrDefault(self.outputCol)
def _create_model(self, java_model): try: model = PipelineModel(java_obj=java_model) model._transfer_params_from_java() except TypeError: model = PipelineModel._from_java(java_model) return model def _fit(self, dataset): java_model = self._fit_java(dataset) return self._create_model(java_model)