Source code for synapse.ml.train.TrainClassifier

# 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
from synapse.ml.train.TrainedClassifierModel import TrainedClassifierModel

[docs]@inherit_doc class TrainClassifier(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator): """ Args: featuresCol (str): The name of the features column inputCols (list): The names of the input columns labelCol (str): The name of the label column labels (list): Sorted label values on the labels column model (object): Classifier to run numFeatures (int): Number of features to hash to reindexLabel (bool): Re-index the label column """ featuresCol = Param(Params._dummy(), "featuresCol", "The name of the features column", typeConverter=TypeConverters.toString) inputCols = Param(Params._dummy(), "inputCols", "The names of the input columns", typeConverter=TypeConverters.toListString) labelCol = Param(Params._dummy(), "labelCol", "The name of the label column", typeConverter=TypeConverters.toString) labels = Param(Params._dummy(), "labels", "Sorted label values on the labels column", typeConverter=TypeConverters.toListString) model = Param(Params._dummy(), "model", "Classifier to run") numFeatures = Param(Params._dummy(), "numFeatures", "Number of features to hash to", typeConverter=TypeConverters.toInt) reindexLabel = Param(Params._dummy(), "reindexLabel", "Re-index the label column", typeConverter=TypeConverters.toBoolean) @keyword_only def __init__( self, java_obj=None, featuresCol="TrainClassifier_9c2492e14838_features", inputCols=None, labelCol=None, labels=None, model=None, numFeatures=0, reindexLabel=True ): super(TrainClassifier, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.train.TrainClassifier", self.uid) else: self._java_obj = java_obj self._setDefault(featuresCol="TrainClassifier_9c2492e14838_features") self._setDefault(numFeatures=0) self._setDefault(reindexLabel=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, featuresCol="TrainClassifier_9c2492e14838_features", inputCols=None, labelCol=None, labels=None, model=None, numFeatures=0, reindexLabel=True ): """ 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.TrainClassifier"
@staticmethod def _from_java(java_stage): module_name=TrainClassifier.__module__ module_name=module_name.rsplit(".", 1)[0] + ".TrainClassifier" 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 setInputCols(self, value): """ Args: inputCols: The names of the input columns """ self._set(inputCols=value) return self
[docs] def setLabelCol(self, value): """ Args: labelCol: The name of the label column """ self._set(labelCol=value) return self
[docs] def setLabels(self, value): """ Args: labels: Sorted label values on the labels column """ self._set(labels=value) return self
[docs] def setModel(self, value): """ Args: model: Classifier 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 setReindexLabel(self, value): """ Args: reindexLabel: Re-index the label column """ self._set(reindexLabel=value) return self
[docs] def getFeaturesCol(self): """ Returns: featuresCol: The name of the features column """ return self.getOrDefault(self.featuresCol)
[docs] def getInputCols(self): """ Returns: inputCols: The names of the input columns """ return self.getOrDefault(self.inputCols)
[docs] def getLabelCol(self): """ Returns: labelCol: The name of the label column """ return self.getOrDefault(self.labelCol)
[docs] def getLabels(self): """ Returns: labels: Sorted label values on the labels column """ return self.getOrDefault(self.labels)
[docs] def getModel(self): """ Returns: model: Classifier 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)
[docs] def getReindexLabel(self): """ Returns: reindexLabel: Re-index the label column """ return self.getOrDefault(self.reindexLabel)
def _create_model(self, java_model): try: model = TrainedClassifierModel(java_obj=java_model) model._transfer_params_from_java() except TypeError: model = TrainedClassifierModel._from_java(java_model) return model def _fit(self, dataset): java_model = self._fit_java(dataset) return self._create_model(java_model)