Menu
Amazon Machine Learning
Developer Guide (Version Latest)

Data Rearrangement

The data rearrangement functionality enables you to create a datasource that is based on only a portion of the input data that it points to. For example, when you create an ML Model using the Create ML Model wizard in the Amazon ML console, and choose the default evaluation option, Amazon ML automatically reserves 30% of your data for ML model evaluation, and uses the other 70% for training. This functionality is enabled by the Data Rearrangement feature of Amazon ML.

If you are using the Amazon ML API to create datasources, you can specify which part of the input data a new datasource will be based. You do this by passing instructions in the DataRearrangement parameter to the CreateDataSourceFromS3, CreateDataSourceFromRedshift or CreateDataSourceFromRDS APIs. The contents of the DataRearrangement string are a JSON string containing the beginning and end locations of your data, expressed as percentages, a complement flag, and a splitting strategy. For example, the following DataRearrangement string specifies that the first 70% of the data will be used to create the datasource:

Copy
{ "splitting": { "percentBegin": 0, "percentEnd": 70, "complement": false, "strategy": "sequential" } }

DataRearrangement Parameters

To change how Amazon ML creates a datasource, use the follow parameters.

PercentBegin (Optional)

Use percentBegin to indicate where the data for the datasource starts. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

Valid values are 0 to 100, inclusive.

PercentEnd (Optional)

Use percentEnd to indicate where the data for the datasource ends. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

Valid values are 0 to 100, inclusive.

Complement (Optional)

The complement parameter tells 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:

Copy
{ "splitting":{ "percentBegin":0, "percentEnd":25 } }

Datasource for training:

Copy
{ "splitting":{ "percentBegin":0, "percentEnd":25, "complement":"true" } }

Valid values are true and false.

Strategy (Optional)

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 create a datasource from a random selection of the data, 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, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using 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:

Copy
{ "splitting":{ "percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": { "randomSeed":"RANDOMSEED" } } }

Datasource for training:

Copy
{ "splitting":{ "percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": { "randomSeed":"RANDOMSEED" } "complement":"true" } }

Valid values are sequential and random.

(Optional) Strategy:RandomSeed

Amazon ML uses the randomSeed to split the data. The default seed for the API is an empty string. To specify a seed for the random split strategy, pass in a string. For more information about random seeds, see Randomly Splitting Your Data in the Amazon Machine Learning Developer Guide.

For sample code that demonstrates how to use cross-validation with Amazon ML, go to Github Machine Learning Samples.