Struct aws_sdk_machinelearning::model::RedshiftDataSpec
source · [−]#[non_exhaustive]pub struct RedshiftDataSpec {
pub database_information: Option<RedshiftDatabase>,
pub select_sql_query: Option<String>,
pub database_credentials: Option<RedshiftDatabaseCredentials>,
pub s3_staging_location: Option<String>,
pub data_rearrangement: Option<String>,
pub data_schema: Option<String>,
pub data_schema_uri: Option<String>,
}
Expand description
Describes the data specification of an Amazon Redshift DataSource
.
Fields (Non-exhaustive)
This struct is marked as non-exhaustive
Struct { .. }
syntax; cannot be matched against without a wildcard ..
; and struct update syntax will not work.database_information: Option<RedshiftDatabase>
Describes the DatabaseName
and ClusterIdentifier
for an Amazon Redshift DataSource
.
select_sql_query: Option<String>
Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource
.
database_credentials: Option<RedshiftDatabaseCredentials>
Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
s3_staging_location: Option<String>
Describes an Amazon S3 location to store the result set of the SelectSqlQuery
query.
data_rearrangement: Option<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: Option<String>
A JSON string that represents the schema for an Amazon Redshift 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" ] }
data_schema_uri: Option<String>
Describes the schema location for an Amazon Redshift DataSource
.
Implementations
sourceimpl RedshiftDataSpec
impl RedshiftDataSpec
sourcepub fn database_information(&self) -> Option<&RedshiftDatabase>
pub fn database_information(&self) -> Option<&RedshiftDatabase>
Describes the DatabaseName
and ClusterIdentifier
for an Amazon Redshift DataSource
.
sourcepub fn select_sql_query(&self) -> Option<&str>
pub fn select_sql_query(&self) -> Option<&str>
Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource
.
sourcepub fn database_credentials(&self) -> Option<&RedshiftDatabaseCredentials>
pub fn database_credentials(&self) -> Option<&RedshiftDatabaseCredentials>
Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
sourcepub fn s3_staging_location(&self) -> Option<&str>
pub fn s3_staging_location(&self) -> Option<&str>
Describes an Amazon S3 location to store the result set of the SelectSqlQuery
query.
sourcepub fn data_rearrangement(&self) -> Option<&str>
pub fn data_rearrangement(&self) -> Option<&str>
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"}}
sourcepub fn data_schema(&self) -> Option<&str>
pub fn data_schema(&self) -> Option<&str>
A JSON string that represents the schema for an Amazon Redshift 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" ] }
sourcepub fn data_schema_uri(&self) -> Option<&str>
pub fn data_schema_uri(&self) -> Option<&str>
Describes the schema location for an Amazon Redshift DataSource
.
sourceimpl RedshiftDataSpec
impl RedshiftDataSpec
sourcepub fn builder() -> Builder
pub fn builder() -> Builder
Creates a new builder-style object to manufacture RedshiftDataSpec
Trait Implementations
sourceimpl Clone for RedshiftDataSpec
impl Clone for RedshiftDataSpec
sourcefn clone(&self) -> RedshiftDataSpec
fn clone(&self) -> RedshiftDataSpec
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Debug for RedshiftDataSpec
impl Debug for RedshiftDataSpec
sourceimpl PartialEq<RedshiftDataSpec> for RedshiftDataSpec
impl PartialEq<RedshiftDataSpec> for RedshiftDataSpec
sourcefn eq(&self, other: &RedshiftDataSpec) -> bool
fn eq(&self, other: &RedshiftDataSpec) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &RedshiftDataSpec) -> bool
fn ne(&self, other: &RedshiftDataSpec) -> bool
This method tests for !=
.
impl StructuralPartialEq for RedshiftDataSpec
Auto Trait Implementations
impl RefUnwindSafe for RedshiftDataSpec
impl Send for RedshiftDataSpec
impl Sync for RedshiftDataSpec
impl Unpin for RedshiftDataSpec
impl UnwindSafe for RedshiftDataSpec
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcepub fn borrow_mut(&mut self) -> &mut T
pub fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcepub fn to_owned(&self) -> T
pub fn to_owned(&self) -> T
Creates owned data from borrowed data, usually by cloning. Read more
sourcepub fn clone_into(&self, target: &mut T)
pub fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more