# 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_7373e1b9b8ca_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_7373e1b9b8ca_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_7373e1b9b8ca_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 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 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)