Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::TimestampSplit (v0.6.0)

Version 0.6.0

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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::TimestampSplit.

Assigns input data to training, validation, and test sets based on a provided timestamps. The youngest data pieces are assigned to training set, next to validation set, and the oldest to the test set.

Supported only for tabular Datasets.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#key

def key() -> ::String

Returns

• (::String) — Required. The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

#key=

def key=(value) -> ::String

Parameter

• value (::String) — Required. The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

Returns

• (::String) — Required. The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

#test_fraction

def test_fraction() -> ::Float

Returns

• (::Float) — The fraction of the input data that is to be used to evaluate the Model.

#test_fraction=

def test_fraction=(value) -> ::Float

Parameter

• value (::Float) — The fraction of the input data that is to be used to evaluate the Model.

Returns

• (::Float) — The fraction of the input data that is to be used to evaluate the Model.

#training_fraction

def training_fraction() -> ::Float

Returns

• (::Float) — The fraction of the input data that is to be used to train the Model.

#training_fraction=

def training_fraction=(value) -> ::Float

Parameter

• value (::Float) — The fraction of the input data that is to be used to train the Model.

Returns

• (::Float) — The fraction of the input data that is to be used to train the Model.

#validation_fraction

def validation_fraction() -> ::Float

Returns

• (::Float) — The fraction of the input data that is to be used to validate the Model.

#validation_fraction=

def validation_fraction=(value) -> ::Float

Parameter

• value (::Float) — The fraction of the input data that is to be used to validate the Model.

Returns

• (::Float) — The fraction of the input data that is to be used to validate the Model.