Cloud AutoML V1beta1 API - Class Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata (v0.7.0)

Version 0.7.0

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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

Returns

• (::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, TEST among its values, or only have TEST, UNASSIGNED values. In the latter case the rows with UNASSIGNED value 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 as UNASSIGNED. 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

Parameter

• 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, TEST among its values, or only have TEST, UNASSIGNED values. In the latter case the rows with UNASSIGNED value 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 as UNASSIGNED. NOTE: Updates of this field will instantly affect any other users concurrently working with the dataset.

Returns

• (::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, TEST among its values, or only have TEST, UNASSIGNED values. In the latter case the rows with UNASSIGNED value 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 as UNASSIGNED. 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

Returns

• (::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

Parameter

• value (::String) — Output only. The table_spec_id of the primary table of this dataset.

Returns

• (::String) — Output only. The table_spec_id of the primary table of this dataset.

#stats_update_time

def stats_update_time() -> ::Google::Protobuf::Timestamp

Returns

• (::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

Parameter

• 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.

Returns

• (::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}

Returns

• (::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}

Parameter

• 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.

Returns

• (::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

Returns

• (::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

Parameter

• 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.

Returns

• (::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

Returns

• (::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

Parameter

• 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.

Returns

• (::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.