class Aws::MachineLearning::Types::S3DataSpec

Describes the data specification of a `DataSource`.

@note When making an API call, you may pass S3DataSpec

data as a hash:

    {
      data_location_s3: "S3Url", # required
      data_rearrangement: "DataRearrangement",
      data_schema: "DataSchema",
      data_schema_location_s3: "S3Url",
    }

@!attribute [rw] data_location_s3

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.
@return [String]

@!attribute [rw] data_rearrangement

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:

* <b> <code>percentBegin</code> </b>

  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.

* <b> <code>percentEnd</code> </b>

  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.

* <b> <code>complement</code> </b>

  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"\}\}`

* <b> <code>strategy</code> </b>

  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"\}\}`
@return [String]

@!attribute [rw] data_schema

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": \[ "F6" \] \\}
@return [String]

@!attribute [rw] data_schema_location_s3

Describes the schema location in Amazon S3. You must provide either
the `DataSchema` or the `DataSchemaLocationS3`.
@return [String]

Constants

SENSITIVE