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Class: Aws::MachineLearning::Types::RDSDataSpec
- Inherits:
-
Struct
- Object
- Struct
- Aws::MachineLearning::Types::RDSDataSpec
- Defined in:
- (unknown)
Overview
When passing RDSDataSpec as input to an Aws::Client method, you can use a vanilla Hash:
{
database_information: { # required
instance_identifier: "RDSInstanceIdentifier", # required
database_name: "RDSDatabaseName", # required
},
select_sql_query: "RDSSelectSqlQuery", # required
database_credentials: { # required
username: "RDSDatabaseUsername", # required
password: "RDSDatabasePassword", # required
},
s3_staging_location: "S3Url", # required
data_rearrangement: "DataRearrangement",
data_schema: "DataSchema",
data_schema_uri: "S3Url",
resource_role: "EDPResourceRole", # required
service_role: "EDPServiceRole", # required
subnet_id: "EDPSubnetId", # required
security_group_ids: ["EDPSecurityGroupId"], # required
}
The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource
.
Returned by:
Instance Attribute Summary collapse
-
#data_rearrangement ⇒ String
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource
. -
#data_schema ⇒ String
A JSON string that represents the schema for an Amazon RDS
DataSource
. -
#data_schema_uri ⇒ String
The Amazon S3 location of the
DataSchema
. -
#database_credentials ⇒ Types::RDSDatabaseCredentials
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
-
#database_information ⇒ Types::RDSDatabase
Describes the
DatabaseName
andInstanceIdentifier
of an Amazon RDS database. -
#resource_role ⇒ String
The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task.
-
#s3_staging_location ⇒ String
The Amazon S3 location for staging Amazon RDS data.
-
#security_group_ids ⇒ Array<String>
The security group IDs to be used to access a VPC-based RDS DB instance.
-
#select_sql_query ⇒ String
The query that is used to retrieve the observation data for the
DataSource
. -
#service_role ⇒ String
The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3.
-
#subnet_id ⇒ String
The subnet ID to be used to access a VPC-based RDS DB instance.
Instance Attribute Details
#data_rearrangement ⇒ String
A JSON string that represents the splitting and rearrangement processing
to be applied to a DataSource
. If the DataRearrangement
parameter is
not provided, all of the input data is used to create the Datasource
.
There are multiple parameters that control what data is used to create a datasource:
percentBegin
Use
percentBegin
to indicate the beginning of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource.percentEnd
Use
percentEnd
to indicate the end of the range of the data used to create the Datasource. If you do not includepercentBegin
andpercentEnd
, Amazon ML includes all of the data when creating the datasource.complement
The
complement
parameter instructs Amazon ML to use the data that is not included in the range ofpercentBegin
topercentEnd
to create a datasource. Thecomplement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values forpercentBegin
andpercentEnd
, along with thecomplement
parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation:
{"splitting":{"percentBegin":0, "percentEnd":25}}
Datasource for training:
{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
strategy
To change how Amazon ML splits the data for a datasource, use the
strategy
parameter.The default value for the
strategy
parameter issequential
, meaning that Amazon ML takes all of the data records between thepercentBegin
andpercentEnd
parameters for the datasource, in the order that the records appear in the input data.The following two
DataRearrangement
lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
Datasource for training:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the
strategy
parameter torandom
and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number betweenpercentBegin
andpercentEnd
. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two
DataRearrangement
lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
Datasource for training:
{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
#data_schema ⇒ String
A JSON string that represents the schema for an Amazon RDS DataSource
.
The DataSchema
defines the structure of the observation data in the
data file(s) referenced in the DataSource
.
A DataSchema
is not required if you specify a DataSchemaUri
Define your DataSchema
as a series of key-value pairs. attributes
and excludedVariableNames
have an array of key-value pairs for their
value. Use the following format to define your DataSchema
.
{ \"version\": \"1.0\",
\"recordAnnotationFieldName\": \"F1\",
\"recordWeightFieldName\": \"F2\",
\"targetFieldName\": \"F3\",
\"dataFormat\": \"CSV\",
\"dataFileContainsHeader\": true,
\"attributes\": [
{ \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ],
\"excludedVariableNames\": [ \"F6\" ] }
<?oxy_insert_end>
#data_schema_uri ⇒ String
The Amazon S3 location of the DataSchema
.
#database_credentials ⇒ Types::RDSDatabaseCredentials
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
#database_information ⇒ Types::RDSDatabase
Describes the DatabaseName
and InstanceIdentifier
of an Amazon RDS
database.
#resource_role ⇒ String
The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
#s3_staging_location ⇒ String
The Amazon S3 location for staging Amazon RDS data. The data retrieved
from Amazon RDS using SelectSqlQuery
is stored in this location.
#security_group_ids ⇒ Array<String>
The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.
#select_sql_query ⇒ String
The query that is used to retrieve the observation data for the
DataSource
.
#service_role ⇒ String
The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
#subnet_id ⇒ String
The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.