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package Paws::MachineLearning::S3DataSpec; |
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use Moose; |
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has DataLocationS3 => (is => 'ro', isa => 'Str', required => 1); |
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has DataRearrangement => (is => 'ro', isa => 'Str'); |
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has DataSchema => (is => 'ro', isa => 'Str'); |
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has DataSchemaLocationS3 => (is => 'ro', isa => 'Str'); |
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1; |
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### main pod documentation begin ### |
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=head1 NAME |
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Paws::MachineLearning::S3DataSpec |
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=head1 USAGE |
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This class represents one of two things: |
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=head3 Arguments in a call to a service |
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Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. |
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Each attribute should be used as a named argument in the calls that expect this type of object. |
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As an example, if Att1 is expected to be a Paws::MachineLearning::S3DataSpec object: |
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$service_obj->Method(Att1 => { DataLocationS3 => $value, ..., DataSchemaLocationS3 => $value }); |
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=head3 Results returned from an API call |
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Use accessors for each attribute. If Att1 is expected to be an Paws::MachineLearning::S3DataSpec object: |
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$result = $service_obj->Method(...); |
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$result->Att1->DataLocationS3 |
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=head1 DESCRIPTION |
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Describes the data specification of a C<DataSource>. |
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=head1 ATTRIBUTES |
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=head2 B<REQUIRED> DataLocationS3 => Str |
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The location of the data file(s) used by a C<DataSource>. The URI |
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specifies a data file or an Amazon Simple Storage Service (Amazon S3) |
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directory or bucket containing data files. |
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=head2 DataRearrangement => Str |
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A JSON string that represents the splitting and rearrangement |
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processing to be applied to a C<DataSource>. If the |
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C<DataRearrangement> parameter is not provided, all of the input data |
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is used to create the C<Datasource>. |
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There are multiple parameters that control what data is used to create |
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a datasource: |
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=over |
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=item * |
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B<C<percentBegin>> |
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Use C<percentBegin> to indicate the beginning of the range of the data |
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used to create the Datasource. If you do not include C<percentBegin> |
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and C<percentEnd>, Amazon ML includes all of the data when creating the |
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datasource. |
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=item * |
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B<C<percentEnd>> |
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Use C<percentEnd> to indicate the end of the range of the data used to |
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create the Datasource. If you do not include C<percentBegin> and |
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C<percentEnd>, Amazon ML includes all of the data when creating the |
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datasource. |
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=item * |
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B<C<complement>> |
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The C<complement> parameter instructs Amazon ML to use the data that is |
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not included in the range of C<percentBegin> to C<percentEnd> to create |
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a datasource. The C<complement> parameter is useful if you need to |
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create complementary datasources for training and evaluation. To create |
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a complementary datasource, use the same values for C<percentBegin> and |
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C<percentEnd>, along with the C<complement> parameter. |
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For example, the following two datasources do not share any data, and |
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can be used to train and evaluate a model. The first datasource has 25 |
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percent of the data, and the second one has 75 percent of the data. |
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Datasource for evaluation: C<{"splitting":{"percentBegin":0, |
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"percentEnd":25}}> |
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Datasource for training: C<{"splitting":{"percentBegin":0, |
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"percentEnd":25, "complement":"true"}}> |
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=item * |
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B<C<strategy>> |
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To change how Amazon ML splits the data for a datasource, use the |
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C<strategy> parameter. |
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The default value for the C<strategy> parameter is C<sequential>, |
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meaning that Amazon ML takes all of the data records between the |
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C<percentBegin> and C<percentEnd> parameters for the datasource, in the |
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order that the records appear in the input data. |
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The following two C<DataRearrangement> lines are examples of |
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sequentially ordered training and evaluation datasources: |
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Datasource for evaluation: C<{"splitting":{"percentBegin":70, |
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"percentEnd":100, "strategy":"sequential"}}> |
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Datasource for training: C<{"splitting":{"percentBegin":70, |
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"percentEnd":100, "strategy":"sequential", "complement":"true"}}> |
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To randomly split the input data into the proportions indicated by the |
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percentBegin and percentEnd parameters, set the C<strategy> parameter |
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to C<random> and provide a string that is used as the seed value for |
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the random data splitting (for example, you can use the S3 path to your |
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data as the random seed string). If you choose the random split |
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strategy, Amazon ML assigns each row of data a pseudo-random number |
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between 0 and 100, and then selects the rows that have an assigned |
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number between C<percentBegin> and C<percentEnd>. Pseudo-random numbers |
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are assigned using both the input seed string value and the byte offset |
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as a seed, so changing the data results in a different split. Any |
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existing ordering is preserved. The random splitting strategy ensures |
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that variables in the training and evaluation data are distributed |
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similarly. It is useful in the cases where the input data may have an |
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implicit sort order, which would otherwise result in training and |
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evaluation datasources containing non-similar data records. |
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The following two C<DataRearrangement> lines are examples of |
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non-sequentially ordered training and evaluation datasources: |
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Datasource for evaluation: C<{"splitting":{"percentBegin":70, |
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"percentEnd":100, "strategy":"random", |
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"randomSeed"="s3://my_s3_path/bucket/file.csv"}}> |
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Datasource for training: C<{"splitting":{"percentBegin":70, |
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"percentEnd":100, "strategy":"random", |
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"randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}> |
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=back |
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=head2 DataSchema => Str |
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A JSON string that represents the schema for an Amazon S3 |
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C<DataSource>. The C<DataSchema> defines the structure of the |
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observation data in the data file(s) referenced in the C<DataSource>. |
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You must provide either the C<DataSchema> or the |
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C<DataSchemaLocationS3>. |
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Define your C<DataSchema> as a series of key-value pairs. C<attributes> |
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and C<excludedVariableNames> have an array of key-value pairs for their |
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value. Use the following format to define your C<DataSchema>. |
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{ "version": "1.0", |
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"recordAnnotationFieldName": "F1", |
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"recordWeightFieldName": "F2", |
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"targetFieldName": "F3", |
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"dataFormat": "CSV", |
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"dataFileContainsHeader": true, |
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"attributes": [ |
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{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", |
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"fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": |
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"CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { |
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"fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", |
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"fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": |
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"WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": |
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"WEIGHTED_STRING_SEQUENCE" } ], |
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"excludedVariableNames": [ "F6" ] } |
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=head2 DataSchemaLocationS3 => Str |
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Describes the schema location in Amazon S3. You must provide either the |
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C<DataSchema> or the C<DataSchemaLocationS3>. |
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=head1 SEE ALSO |
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This class forms part of L<Paws>, describing an object used in L<Paws::MachineLearning> |
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=head1 BUGS and CONTRIBUTIONS |
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The source code is located here: https://github.com/pplu/aws-sdk-perl |
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Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues |
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=cut |
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