Source code for synapse.ml.train.TrainRegressor

# 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 synapse.ml.train.TrainedRegressorModel import TrainedRegressorModel

[docs]@inherit_doc class TrainRegressor(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator): """ Args: featuresCol (object): The name of the features column labelCol (object): The name of the label column model (object): Regressor to run numFeatures (int): Number of features to hash to """ featuresCol = Param(Params._dummy(), "featuresCol", "The name of the features column") labelCol = Param(Params._dummy(), "labelCol", "The name of the label column") model = Param(Params._dummy(), "model", "Regressor to run") numFeatures = Param(Params._dummy(), "numFeatures", "Number of features to hash to", typeConverter=TypeConverters.toInt) @keyword_only def __init__( self, java_obj=None, featuresCol="TrainRegressor_f82723c57a77_features", labelCol=None, model=None, numFeatures=0 ): super(TrainRegressor, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.train.TrainRegressor", self.uid) else: self._java_obj = java_obj self._setDefault(featuresCol="TrainRegressor_f82723c57a77_features") self._setDefault(numFeatures=0) 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, featuresCol="TrainRegressor_f82723c57a77_features", labelCol=None, model=None, numFeatures=0 ): """ 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.train.TrainRegressor"
@staticmethod def _from_java(java_stage): module_name=TrainRegressor.__module__ module_name=module_name.rsplit(".", 1)[0] + ".TrainRegressor" return from_java(java_stage, module_name)
[docs] def setFeaturesCol(self, value): """ Args: featuresCol: The name of the features column """ self._set(featuresCol=value) return self
[docs] def setLabelCol(self, value): """ Args: labelCol: The name of the label column """ self._set(labelCol=value) return self
[docs] def setModel(self, value): """ Args: model: Regressor to run """ self._set(model=value) return self
[docs] def setNumFeatures(self, value): """ Args: numFeatures: Number of features to hash to """ self._set(numFeatures=value) return self
[docs] def getFeaturesCol(self): """ Returns: featuresCol: The name of the features column """ return self.getOrDefault(self.featuresCol)
[docs] def getLabelCol(self): """ Returns: labelCol: The name of the label column """ return self.getOrDefault(self.labelCol)
[docs] def getModel(self): """ Returns: model: Regressor to run """ return JavaParams._from_java(self._java_obj.getModel())
[docs] def getNumFeatures(self): """ Returns: numFeatures: Number of features to hash to """ return self.getOrDefault(self.numFeatures)
def _create_model(self, java_model): try: model = TrainedRegressorModel(java_obj=java_model) model._transfer_params_from_java() except TypeError: model = TrainedRegressorModel._from_java(java_model) return model def _fit(self, dataset): java_model = self._fit_java(dataset) return self._create_model(java_model)