Reference documentation and code samples for the Cloud AutoML V1beta1 API class Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata.
Metadata for a dataset used for AutoML Tables.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#ml_use_column_spec_id
def ml_use_column_spec_id() -> ::String-
(::String) — column_spec_id of the primary table column which specifies a possible ML
use of the row, i.e. the column will be used to split the rows into TRAIN,
VALIDATE and TEST sets.
Required type: STRING.
This column, if set, must either have all of
TRAIN,VALIDATE,TESTamong its values, or only haveTEST,UNASSIGNEDvalues. In the latter case the rows withUNASSIGNEDvalue will be assigned by AutoML. Note that if a given ml use distribution makes it impossible to create a "good" model, that call will error describing the issue. If both this column_spec_id and primary table's time_column_spec_id are not set, then all rows are treated asUNASSIGNED. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
#ml_use_column_spec_id=
def ml_use_column_spec_id=(value) -> ::String-
value (::String) — column_spec_id of the primary table column which specifies a possible ML
use of the row, i.e. the column will be used to split the rows into TRAIN,
VALIDATE and TEST sets.
Required type: STRING.
This column, if set, must either have all of
TRAIN,VALIDATE,TESTamong its values, or only haveTEST,UNASSIGNEDvalues. In the latter case the rows withUNASSIGNEDvalue will be assigned by AutoML. Note that if a given ml use distribution makes it impossible to create a "good" model, that call will error describing the issue. If both this column_spec_id and primary table's time_column_spec_id are not set, then all rows are treated asUNASSIGNED. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
-
(::String) — column_spec_id of the primary table column which specifies a possible ML
use of the row, i.e. the column will be used to split the rows into TRAIN,
VALIDATE and TEST sets.
Required type: STRING.
This column, if set, must either have all of
TRAIN,VALIDATE,TESTamong its values, or only haveTEST,UNASSIGNEDvalues. In the latter case the rows withUNASSIGNEDvalue will be assigned by AutoML. Note that if a given ml use distribution makes it impossible to create a "good" model, that call will error describing the issue. If both this column_spec_id and primary table's time_column_spec_id are not set, then all rows are treated asUNASSIGNED. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
#primary_table_spec_id
def primary_table_spec_id() -> ::String- (::String) — Output only. The table_spec_id of the primary table of this dataset.
#primary_table_spec_id=
def primary_table_spec_id=(value) -> ::String- value (::String) — Output only. The table_spec_id of the primary table of this dataset.
- (::String) — Output only. The table_spec_id of the primary table of this dataset.
#stats_update_time
def stats_update_time() -> ::Google::Protobuf::Timestamp- (::Google::Protobuf::Timestamp) — Output only. The most recent timestamp when target_column_correlations field and all descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns fields were last (re-)generated. Any changes that happened to the dataset afterwards are not reflected in these fields values. The regeneration happens in the background on a best effort basis.
#stats_update_time=
def stats_update_time=(value) -> ::Google::Protobuf::Timestamp- value (::Google::Protobuf::Timestamp) — Output only. The most recent timestamp when target_column_correlations field and all descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns fields were last (re-)generated. Any changes that happened to the dataset afterwards are not reflected in these fields values. The regeneration happens in the background on a best effort basis.
- (::Google::Protobuf::Timestamp) — Output only. The most recent timestamp when target_column_correlations field and all descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns fields were last (re-)generated. Any changes that happened to the dataset afterwards are not reflected in these fields values. The regeneration happens in the background on a best effort basis.
#target_column_correlations
def target_column_correlations() -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AutoML::V1beta1::CorrelationStats}-
(::Google::Protobuf::Map{::String => ::Google::Cloud::AutoML::V1beta1::CorrelationStats}) — Output only. Correlations between
TablesDatasetMetadata.target_column_spec_id, and other columns of the
TablesDatasetMetadataprimary_table. Only set if the target column is set. Mapping from other column spec id to its CorrelationStats with the target column. This field may be stale, see the stats_update_time field for for the timestamp at which these stats were last updated.
#target_column_correlations=
def target_column_correlations=(value) -> ::Google::Protobuf::Map{::String => ::Google::Cloud::AutoML::V1beta1::CorrelationStats}-
value (::Google::Protobuf::Map{::String => ::Google::Cloud::AutoML::V1beta1::CorrelationStats}) — Output only. Correlations between
TablesDatasetMetadata.target_column_spec_id, and other columns of the
TablesDatasetMetadataprimary_table. Only set if the target column is set. Mapping from other column spec id to its CorrelationStats with the target column. This field may be stale, see the stats_update_time field for for the timestamp at which these stats were last updated.
-
(::Google::Protobuf::Map{::String => ::Google::Cloud::AutoML::V1beta1::CorrelationStats}) — Output only. Correlations between
TablesDatasetMetadata.target_column_spec_id, and other columns of the
TablesDatasetMetadataprimary_table. Only set if the target column is set. Mapping from other column spec id to its CorrelationStats with the target column. This field may be stale, see the stats_update_time field for for the timestamp at which these stats were last updated.
#target_column_spec_id
def target_column_spec_id() -> ::String-
(::String) — column_spec_id of the primary table's column that should be used as the
training & prediction target.
This column must be non-nullable and have one of following data types
(otherwise model creation will error):
CATEGORY
FLOAT64
If the type is CATEGORY , only up to 100 unique values may exist in that column across all rows.
NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
#target_column_spec_id=
def target_column_spec_id=(value) -> ::String-
value (::String) — column_spec_id of the primary table's column that should be used as the
training & prediction target.
This column must be non-nullable and have one of following data types
(otherwise model creation will error):
CATEGORY
FLOAT64
If the type is CATEGORY , only up to 100 unique values may exist in that column across all rows.
NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
-
(::String) — column_spec_id of the primary table's column that should be used as the
training & prediction target.
This column must be non-nullable and have one of following data types
(otherwise model creation will error):
CATEGORY
FLOAT64
If the type is CATEGORY , only up to 100 unique values may exist in that column across all rows.
NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
#weight_column_spec_id
def weight_column_spec_id() -> ::String- (::String) — column_spec_id of the primary table's column that should be used as the weight column, i.e. the higher the value the more important the row will be during model training. Required type: FLOAT64. Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is ignored for training. If not set all rows are assumed to have equal weight of 1. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
#weight_column_spec_id=
def weight_column_spec_id=(value) -> ::String- value (::String) — column_spec_id of the primary table's column that should be used as the weight column, i.e. the higher the value the more important the row will be during model training. Required type: FLOAT64. Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is ignored for training. If not set all rows are assumed to have equal weight of 1. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.
- (::String) — column_spec_id of the primary table's column that should be used as the weight column, i.e. the higher the value the more important the row will be during model training. Required type: FLOAT64. Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is ignored for training. If not set all rows are assumed to have equal weight of 1. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.