@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class S3DataSpec extends Object implements Serializable, Cloneable, StructuredPojo
Describes the data specification of a DataSource.
| Constructor and Description |
|---|
S3DataSpec() |
| Modifier and Type | Method and Description |
|---|---|
S3DataSpec |
clone() |
boolean |
equals(Object obj) |
String |
getDataLocationS3()
The location of the data file(s) used by a
DataSource. |
String |
getDataRearrangement()
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource. |
String |
getDataSchema()
A JSON string that represents the schema for an Amazon S3
DataSource. |
String |
getDataSchemaLocationS3()
Describes the schema location in Amazon S3.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setDataLocationS3(String dataLocationS3)
The location of the data file(s) used by a
DataSource. |
void |
setDataRearrangement(String dataRearrangement)
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource. |
void |
setDataSchema(String dataSchema)
A JSON string that represents the schema for an Amazon S3
DataSource. |
void |
setDataSchemaLocationS3(String dataSchemaLocationS3)
Describes the schema location in Amazon S3.
|
String |
toString()
Returns a string representation of this object.
|
S3DataSpec |
withDataLocationS3(String dataLocationS3)
The location of the data file(s) used by a
DataSource. |
S3DataSpec |
withDataRearrangement(String dataRearrangement)
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource. |
S3DataSpec |
withDataSchema(String dataSchema)
A JSON string that represents the schema for an Amazon S3
DataSource. |
S3DataSpec |
withDataSchemaLocationS3(String dataSchemaLocationS3)
Describes the schema location in Amazon S3.
|
public void setDataLocationS3(String dataLocationS3)
The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon
Simple Storage Service (Amazon S3) directory or bucket containing data files.
dataLocationS3 - The location of the data file(s) used by a DataSource. The URI specifies a data file or an
Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.public String getDataLocationS3()
The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon
Simple Storage Service (Amazon S3) directory or bucket containing data files.
DataSource. The URI specifies a data file or an
Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.public S3DataSpec withDataLocationS3(String dataLocationS3)
The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon
Simple Storage Service (Amazon S3) directory or bucket containing data files.
dataLocationS3 - The location of the data file(s) used by a DataSource. The URI specifies a data file or an
Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.public void setDataRearrangement(String dataRearrangement)
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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of
percentBegin to percentEnd to create a datasource. The complement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a
complementary datasource, use the same values for percentBegin and percentEnd, along
with the complement 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 is sequential, meaning that Amazon ML
takes all of the data records between the percentBegin and percentEnd 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 to random 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 between percentBegin and
percentEnd. 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"}}
dataRearrangement - 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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of percentBegin to percentEnd to create a datasource. The
complement parameter is useful if you need to create complementary datasources for training
and evaluation. To create a complementary datasource, use the same values for percentBegin
and percentEnd, along with the complement 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 is sequential, meaning that Amazon
ML takes all of the data records between the percentBegin and percentEnd
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 to random 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 between
percentBegin and percentEnd. 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"}}
public String getDataRearrangement()
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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of
percentBegin to percentEnd to create a datasource. The complement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a
complementary datasource, use the same values for percentBegin and percentEnd, along
with the complement 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 is sequential, meaning that Amazon ML
takes all of the data records between the percentBegin and percentEnd 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 to random 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 between percentBegin and
percentEnd. 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"}}
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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of percentBegin to percentEnd to create a datasource. The
complement parameter is useful if you need to create complementary datasources for training
and evaluation. To create a complementary datasource, use the same values for percentBegin
and percentEnd, along with the complement 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 is sequential, meaning that Amazon
ML takes all of the data records between the percentBegin and percentEnd
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 to random 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 between
percentBegin and percentEnd. 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"}}
public S3DataSpec withDataRearrangement(String dataRearrangement)
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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of
percentBegin to percentEnd to create a datasource. The complement
parameter is useful if you need to create complementary datasources for training and evaluation. To create a
complementary datasource, use the same values for percentBegin and percentEnd, along
with the complement 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 is sequential, meaning that Amazon ML
takes all of the data records between the percentBegin and percentEnd 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 to random 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 between percentBegin and
percentEnd. 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"}}
dataRearrangement - 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 include percentBegin and percentEnd, 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 include percentBegin and percentEnd, 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 of percentBegin to percentEnd to create a datasource. The
complement parameter is useful if you need to create complementary datasources for training
and evaluation. To create a complementary datasource, use the same values for percentBegin
and percentEnd, along with the complement 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 is sequential, meaning that Amazon
ML takes all of the data records between the percentBegin and percentEnd
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 to random 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 between
percentBegin and percentEnd. 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"}}
public void setDataSchema(String dataSchema)
A JSON string that represents the schema for an Amazon S3 DataSource. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource.
You must provide either the DataSchema or the DataSchemaLocationS3.
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" ] }
dataSchema - A JSON string that represents the schema for an Amazon S3 DataSource. The
DataSchema defines the structure of the observation data in the data file(s) referenced in
the DataSource.
You must provide either the DataSchema or the DataSchemaLocationS3.
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" ] }
public String getDataSchema()
A JSON string that represents the schema for an Amazon S3 DataSource. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource.
You must provide either the DataSchema or the DataSchemaLocationS3.
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" ] }
DataSource. The
DataSchema defines the structure of the observation data in the data file(s) referenced in
the DataSource.
You must provide either the DataSchema or the DataSchemaLocationS3.
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" ] }
public S3DataSpec withDataSchema(String dataSchema)
A JSON string that represents the schema for an Amazon S3 DataSource. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource.
You must provide either the DataSchema or the DataSchemaLocationS3.
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" ] }
dataSchema - A JSON string that represents the schema for an Amazon S3 DataSource. The
DataSchema defines the structure of the observation data in the data file(s) referenced in
the DataSource.
You must provide either the DataSchema or the DataSchemaLocationS3.
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" ] }
public void setDataSchemaLocationS3(String dataSchemaLocationS3)
Describes the schema location in Amazon S3. You must provide either the DataSchema or the
DataSchemaLocationS3.
dataSchemaLocationS3 - Describes the schema location in Amazon S3. You must provide either the DataSchema or the
DataSchemaLocationS3.public String getDataSchemaLocationS3()
Describes the schema location in Amazon S3. You must provide either the DataSchema or the
DataSchemaLocationS3.
DataSchema or the
DataSchemaLocationS3.public S3DataSpec withDataSchemaLocationS3(String dataSchemaLocationS3)
Describes the schema location in Amazon S3. You must provide either the DataSchema or the
DataSchemaLocationS3.
dataSchemaLocationS3 - Describes the schema location in Amazon S3. You must provide either the DataSchema or the
DataSchemaLocationS3.public String toString()
toString in class ObjectObject.toString()public S3DataSpec clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojoProtocolMarshaller.marshall in interface StructuredPojoprotocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.