EvalTable

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class EvalTable

columns: 'list[str | int] | None' = None,
data: 'list[Iterable[Any]] | np.ndarray | pd.DataFrame | None' = None,
rows: 'list[Iterable[Any]] | None' = None,
dataframe: 'pd.DataFrame | None' = None,
dtype: 'Any' = None,
optional: 'bool | list[bool]' = True,
allow_mixed_types: 'bool' = False,
log_mode: "Literal['IMMUTABLE']" = 'IMMUTABLE', *,
input_columns: 'list[str] | None' = None,
output_columns: 'list[str] | None' = None,
score_columns: 'list[str] | None' = None,
unsupported_media_mode: 'media_adapters.UnsupportedMediaMode' = 'stub'

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Description

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Args

• columns: Names of the columns in the table. If unset, but input_columns, output_columns, or score_columns are set, then we’ll just set columns to the union of those, in that order.
• data:
• rows:
• dataframe:
• dtype:
• optional:
• allow_mixed_types:
• log_mode: Controls how the table is logged when the same EvalTable is passed to run.log() more than once.
  • “IMMUTABLE” (default): full table logged on first run.log(); subsequent run.log() calls are no-ops.
  • “MUTABLE” and “INCREMENTAL”: not currently supported for EvalTable.
• input_columns: Names of the input columns. If set, designates these columns as inputs. Eval comparisons will match rows based on matching values from input columns. If unset, we will inject a “row” index input column so comparisons can match against that.
• output_columns: Names of the output columns. These represents the values to be compared. Any columns not designated as input, output, or score will default to being output columns.
• score_columns: Names of the score columns. These represent derived scores for the outputs. By default, we will auto-summarize any numeric and boolean scores.
• unsupported_media_mode: How to handle unsupported wandb media/value types.
  • “stub” (default): log unsupported values as short placeholder strings like “[wandb.Html not yet supported]”. (This is a temporary flag for use during development.)
  • “raise”: fail fast when unsupported wandb value types are added.

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Methods

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method EvalTable.add_column()

self,
name: 'str | int',
data: 'list[Any] | np.ndarray',
optional: 'bool' = False

Arguments

• name: (str) - the unique name of the column
• data: (list | np.array) - a column of homogeneous data
• optional: (bool) - if null-like values are permitted

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method Table.add_computed_columns()

(self, fn)

Arguments

• fn: A function which accepts one or two parameters, ndx (int) and row (dict), which is expected to return a dict representing new columns for that row, keyed by the new column names.
  • ndx is an integer representing the index of the row. Only included if include_ndx is set to True.
  • row is a dictionary keyed by existing columns

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method EvalTable.add_data()

self, *data: 'Any'

Arguments

• data:

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method Table.add_row()

(self, *row)

Arguments

• row:

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method Table.cast()

(self, col_name, dtype, optional=False)

Arguments

• col_name: The name of the column to cast.
• dtype: The target dtype.
• optional: If the column should allow Nones.

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method Table.get_column()

(self, name, convert_to=None)

Arguments

• name: (str) - the name of the column
• convert_to: (str, optional)
  • “numpy”: will convert the underlying data to numpy object

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method Table.get_dataframe()

(self)

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method Table.get_index()

(self)