Class TablesDatasetMetadata (2.4.2)

TablesDatasetMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Metadata for a dataset used for AutoML Tables. .. attribute:: primary_table_spec_id

Output only. The table_spec_id of the primary table of this dataset.

:type: str

Attributes

Name	Description
`target_column_spec_id`	`str` 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`	`str` 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.
`ml_use_column_spec_id`	`str` 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.
`target_column_correlations`	`Sequence[google.cloud.automl_v1beta1.types.TablesDatasetMetadata.TargetColumnCorrelationsEntry]` 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.
`stats_update_time`	`google.protobuf.timestamp_pb2.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.

Classes

TargetColumnCorrelationsEntry

TargetColumnCorrelationsEntry(
    mapping=None, *, ignore_unknown_fields=False, **kwargs
)

The abstract base class for a message.

Parameters

Name	Description
`kwargs`	`dict` Keys and values corresponding to the fields of the message.
`mapping`	`Union[dict, .Message]` A dictionary or message to be used to determine the values for this message.
`ignore_unknown_fields`	`Optional(bool)` If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.