B !`d @sHdZddlmZddlmZddlmZddlZGdddejdZdS) z)Base Class of Experiment Data Access API.)absolute_import)division)print_functionNc@s eZdZdZejdddZdS)BaseExperimentz&Base class for experiment data access.NFcCsdS)uPExport scalar data as a pandas.DataFrame. Args: runs_filter: A regex filter for runs (e.g., r'run_[2-4]'). Operates in logical AND relation with `tags_filter`. tags_filter: A regex filter for tags (e.g., r'.*loss.*'). Operates in logical AND related with `runs_filter`. pivot: Whether to returned DataFrame will be pivoted (via pandas’ `pivot_data()` method to a “wide” format wherein the tags of a given run and a given step are all collected in a single row. Setting `pivot` to `True` stipulates that the sets of step values are identical among all tags in every run of the experiment (after any run and tag filtering), so that the pivoting operation will not introduce missing values in the resultant DataFrame. Failing to meet this condition will cause `pivot=True` to raise a `ValueError`. If not provided, defaults to `False`. include_wall_time: Include wall_time (timestamps in nanoseconds since the epoch in float64) as a column in the returned DataFrame. If not provided, defaults to `False`. Returns: If `pivot` (default): A pivoted DataFrame with the indexing columns of - run - step And value columns that correspond to the tags. Duplicate entries for each run-step combination will be aggregated with `numpy.stack`. This format is more friendly to manipulation and plotting and hence io chosen as the default. When certain rows have missing values, a warning message will be displayed and advise the user to use the `pivot=False` if steps have different meanings in the experiment. If `not pivot`: A DataFrame with the following columns. - run: (non-null object) - tag: (non-null object) - steps: (non-null int64) - wall_time: (non-null object) - value: (non-null float32) N)selfZ runs_filterZ tags_filterZpivotZinclude_wall_timerr\/home/dcms/DCMS/lib/python3.7/site-packages/tensorboard/data/experimental/base_experiment.py get_scalarss1zBaseExperiment.get_scalars)NNFF)__name__ __module__ __qualname____doc__abcabstractmethodr rrrrrs r) metaclass)r __future__rrrrABCMetarrrrrs