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, wheretime-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, wheretime-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, wheretime-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.