def __init__(
self,
trainingInstanceType,
trainingInstanceCount,
endpointInstanceType,
endpointInitialInstanceCount,
sagemakerRole=IAMRoleFromConfig(),
requestRowSerializer=ProtobufRequestRowSerializer(),
responseRowDeserializer=LinearLearnerBinaryClassifierProtobufResponseRowDeserializer(),
trainingInputS3DataPath=S3AutoCreatePath(),
trainingOutputS3DataPath=S3AutoCreatePath(),
trainingInstanceVolumeSizeInGB=1024,
trainingProjectedColumns=None,
trainingChannelName="train",
trainingContentType=None,
trainingS3DataDistribution="ShardedByS3Key",
trainingSparkDataFormat="sagemaker",
trainingSparkDataFormatOptions=None,
trainingInputMode="File",
trainingCompressionCodec=None,
trainingMaxRuntimeInSeconds=24*60*60,
trainingKmsKeyId=None,
modelEnvironmentVariables=None,
endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_CONSTRUCT,
sagemakerClient=SageMakerClients.create_sagemaker_client(),
region=None,
s3Client=SageMakerClients.create_s3_default_client(),
stsClient=SageMakerClients.create_sts_default_client(),
modelPrependInputRowsToTransformationRows=True,
deleteStagingDataAfterTraining=True,
namePolicyFactory=RandomNamePolicyFactory(),
uid=None,
javaObject=None):
if trainingSparkDataFormatOptions is None:
trainingSparkDataFormatOptions = {}
if modelEnvironmentVariables is None:
modelEnvironmentVariables = {}
if uid is None:
uid = Identifiable._randomUID()
kwargs = locals().copy()
del kwargs['self']
super(LinearLearnerBinaryClassifier, self).__init__(**kwargs)
default_params = {
'predictor_type': 'binary_classifier'
}
self._setDefault(**default_params)
def test_can_create_classifier_from_configured_iam_role():
training_instance_type = "c4.8xlarge"
training_instance_count = 3
endpoint_instance_type = "c4.8xlarge"
endpoint_initial_instance_count = 3
estimator = LinearLearnerBinaryClassifier(
trainingInstanceType=training_instance_type,
trainingInstanceCount=training_instance_count,
endpointInstanceType=endpoint_instance_type,
endpointInitialInstanceCount=endpoint_initial_instance_count,
trainingProjectedColumns=None,
trainingS3DataDistribution="by-key",
trainingInputMode="File",
endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_TRANSFORM,
modelPrependInputRowsToTransformationRows=True,
namePolicyFactory=RandomNamePolicyFactory(),
uid="sagemaker")
return estimator
def get_linear_learner_regressor():
training_instance_type = "c4.8xlarge"
training_instance_count = 3
endpoint_instance_type = "c4.8xlarge"
endpoint_initial_instance_count = 3
estimator = LinearLearnerRegressor(
trainingInstanceType=training_instance_type,
trainingInstanceCount=training_instance_count,
endpointInstanceType=endpoint_instance_type,
endpointInitialInstanceCount=endpoint_initial_instance_count,
sagemakerRole=IAMRole("some-role"),
trainingProjectedColumns=None,
trainingS3DataDistribution="by-key",
trainingInputMode="File",
endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_TRANSFORM,
modelPrependInputRowsToTransformationRows=True,
namePolicyFactory=RandomNamePolicyFactory(),
uid="sagemaker")
return estimator
def test_linearLearnerBinaryClassifier_passes_correct_params_to_scala():
training_instance_type = "c4.8xlarge"
training_instance_count = 3
endpoint_instance_type = "c4.8xlarge"
endpoint_initial_instance_count = 3
training_bucket = "random-bucket"
input_prefix = "linear-learner-binary-classifier-training"
output_prefix = "linear-learner-binary-classifier-out"
integTestingRole = "arn:aws:iam::123456789:role/SageMakerRole"
estimator = LinearLearnerBinaryClassifier(
trainingInstanceType=training_instance_type,
trainingInstanceCount=training_instance_count,
endpointInstanceType=endpoint_instance_type,
endpointInitialInstanceCount=endpoint_initial_instance_count,
sagemakerRole=IAMRole(integTestingRole),
requestRowSerializer=ProtobufRequestRowSerializer(),
responseRowDeserializer=
LinearLearnerBinaryClassifierProtobufResponseRowDeserializer(),
trainingInstanceVolumeSizeInGB=2048,
trainingInputS3DataPath=S3DataPath(training_bucket, input_prefix),
trainingOutputS3DataPath=S3DataPath(training_bucket, output_prefix),
trainingMaxRuntimeInSeconds=1,
endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_TRANSFORM,
sagemakerClient=SageMakerClients.create_sagemaker_client(),
s3Client=SageMakerClients.create_s3_default_client(),
stsClient=SageMakerClients.create_sts_default_client(),
modelPrependInputRowsToTransformationRows=True,
namePolicyFactory=RandomNamePolicyFactory(),
uid="sagemaker")
assert estimator.trainingInputS3DataPath.bucket == training_bucket
assert estimator.trainingInputS3DataPath.objectPath == input_prefix
assert estimator.trainingInstanceCount == training_instance_count
assert estimator.trainingInstanceType == training_instance_type
assert estimator.endpointInstanceType == endpoint_instance_type
assert estimator.endpointInitialInstanceCount == endpoint_initial_instance_count
assert estimator.trainingInstanceVolumeSizeInGB == 2048
assert estimator.trainingMaxRuntimeInSeconds == 1
assert estimator.trainingKmsKeyId is None
estimator = SageMakerEstimator(
trainingImage=get_image_uri(region, 'kmeans'), # Training image
modelImage=get_image_uri(region, 'kmeans'), # Model image
requestRowSerializer=ProtobufRequestRowSerializer(),
responseRowDeserializer=KMeansProtobufResponseRowDeserializer(),
hyperParameters={
"k": "10",
"feature_dim": "784"
}, # Set parameters for K-Means
sagemakerRole=IAMRole(role),
trainingInstanceType="ml.m4.xlarge",
trainingInstanceCount=1,
endpointInstanceType="ml.t2.medium",
endpointInitialInstanceCount=1,
trainingSparkDataFormat="sagemaker",
namePolicyFactory=RandomNamePolicyFactory("sparksm-4-"),
endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_TRANSFORM)
customModel = estimator.fit(trainingData)
#Inference
transformedData = customModel.transform(testData)
transformedData.show()
from pyspark.sql.types import DoubleType
import matplotlib.pyplot as plt
import numpy as np
import string
# Helper function to display a digit
from pyspark.ml import Pipeline
from sagemaker_pyspark.algorithms import PCASageMakerEstimator, KMeansSageMakerEstimator
from sagemaker_pyspark import RandomNamePolicyFactory, IAMRole, EndpointCreationPolicy
from sagemaker_pyspark.transformation.serializers import ProtobufRequestRowSerializer
# ML pipeline with 2 stages: PCA and K-Means
# 1st stage: PCA on SageMaker
pcaSageMakerEstimator = PCASageMakerEstimator(
sagemakerRole=IAMRole(role),
trainingInstanceType="ml.m4.xlarge",
trainingInstanceCount=1,
endpointInstanceType="ml.t2.large",
endpointInitialInstanceCount=1,
namePolicyFactory=RandomNamePolicyFactory("sparksm-3p-"))
# Set parameters for PCA (number of features in input and the number of principal components to find)
pcaSageMakerEstimator.setFeatureDim(784)
pcaSageMakerEstimator.setNumComponents(50)
# 2nd stage: K-Means on SageMaker
kMeansSageMakerEstimator = KMeansSageMakerEstimator(
sagemakerRole=IAMRole(role),
trainingSparkDataFormatOptions={
"featuresColumnName": "projection"
}, # Default output column generated by PCASageMakerEstimator
requestRowSerializer=ProtobufRequestRowSerializer(
featuresColumnName="projection"
), # Default output column generated by PCASageMakerEstimator
trainingInstanceType="ml.m4.xlarge",
