Google Cloud Ai Platform V1 Client - Class StratifiedSplit (0.31.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class StratifiedSplit.

Assigns input data to the training, validation, and test sets so that the distribution of values found in the categorical column (as specified by the key field) is mirrored within each split. The fraction values determine the relative sizes of the splits.

For example, if the specified column has three values, with 50% of the rows having value "A", 25% value "B", and 25% value "C", and the split fractions are specified as 80/10/10, then the training set will constitute 80% of the training data, with about 50% of the training set rows having the value "A" for the specified column, about 25% having the value "B", and about 25% having the value "C". Only the top 500 occurring values are used; any values not in the top 500 values are randomly assigned to a split. If less than three rows contain a specific value, those rows are randomly assigned. Supported only for tabular Datasets.

Generated from protobuf message google.cloud.aiplatform.v1.StratifiedSplit

Namespace

Google \ Cloud \ AIPlatform \ V1

Methods

__construct

Constructor.

Parameters
NameDescription
data array

Optional. Data for populating the Message object.

↳ training_fraction float

The fraction of the input data that is to be used to train the Model.

↳ validation_fraction float

The fraction of the input data that is to be used to validate the Model.

↳ test_fraction float

The fraction of the input data that is to be used to evaluate the Model.

↳ key string

Required. The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.

getTrainingFraction

The fraction of the input data that is to be used to train the Model.

Returns
TypeDescription
float

setTrainingFraction

The fraction of the input data that is to be used to train the Model.

Parameter
NameDescription
var float
Returns
TypeDescription
$this

getValidationFraction

The fraction of the input data that is to be used to validate the Model.

Returns
TypeDescription
float

setValidationFraction

The fraction of the input data that is to be used to validate the Model.

Parameter
NameDescription
var float
Returns
TypeDescription
$this

getTestFraction

The fraction of the input data that is to be used to evaluate the Model.

Returns
TypeDescription
float

setTestFraction

The fraction of the input data that is to be used to evaluate the Model.

Parameter
NameDescription
var float
Returns
TypeDescription
$this

getKey

Required. The key is a name of one of the Dataset's data columns.

The key provided must be for a categorical column.

Returns
TypeDescription
string

setKey

Required. The key is a name of one of the Dataset's data columns.

The key provided must be for a categorical column.

Parameter
NameDescription
var string
Returns
TypeDescription
$this