python/ray/tune/analysis/experiment_analysis.py
def dataframe(
        self, metric: Optional[str] = None, mode: Optional[str] = None
    ) -> DataFrame:
        """Returns a pandas.DataFrame object constructed from the trials.

        This function will look through all observed results of each trial
        and return the one corresponding to the passed ``metric`` and
        ``mode``: If ``mode=min``, it returns the result with the lowest
        *ever* observed ``metric`` for this trial (this is not necessarily
        the last)! For ``mode=max``, it's the highest, respectively. If
        ``metric=None`` or ``mode=None``, the last result will be returned.

        Args:
            metric: Key for trial info to order on. If None, uses last result.
            mode: One of [None, "min", "max"].

        Returns:
            pd.DataFrame: Constructed from a result dict of each trial.
        """
        # Do not validate metric/mode here or set from default metric/mode!
        # Otherwise we will get confusing results as the lowest ever observed
        # result may not be the last result.
        if mode and mode not in ["min", "max"]:
            raise ValueError("If set, `mode` has to be one of [min, max]")

        if mode and not metric:
            raise ValueError(
                "If a `mode` is passed to `ExperimentAnalysis.dataframe(),"
                " you'll also have to pass a `metric`!"
            )

        rows = self._retrieve_rows(metric=metric, mode=mode)
        all_configs = self.get_all_configs(prefix=True)
        for path, config in all_configs.items():
            if path in rows:
                rows[path].update(config)
                rows[path].update(logdir=path)
        return pd.DataFrame(list(rows.values()))
Returns a pandas.DataFrame object constructed from the trials.

        This function will look through all observed results of each trial
        and return the one corresponding to the passed ``metric`` and
        ``mode``: If ``mode=min``, it returns the result with the lowest
        *ever* observed ``metric`` for this trial (this is not necessarily
        the last)! For ``mode=max``, it's the highest, respectively. If
        ``metric=None`` or ``mode=None``, the last result will be returned.

        Args:
            metric: Key for trial info to order on. If None, uses last result.
            mode: One of [None, "min", "max"].

        Returns:
            pd.DataFrame: Constructed from a result dict of each trial.
        
Returns a pandas.DataFrame object constructed from the trials.
Returns a pandas . DataFrame object constructed from the trials .
def dataframe(
        self, metric: Optional[str] = None, mode: Optional[str] = None
    ) -> DataFrame:
        if mode and mode not in ["min", "max"]:
            raise ValueError("If set, `mode` has to be one of [min, max]")
        if mode and not metric:
            raise ValueError(
                "If a `mode` is passed to `ExperimentAnalysis.dataframe(),"
                " you'll also have to pass a `metric`!"
            )
        rows = self._retrieve_rows(metric=metric, mode=mode)
        all_configs = self.get_all_configs(prefix=True)
        for path, config in all_configs.items():
            if path in rows:
                rows[path].update(config)
                rows[path].update(logdir=path)
        return pd.DataFrame(list(rows.values()))
def dataframe ( self , metric : Optional [ str ] = None , mode : Optional [ str ] = None ) -> DataFrame : if mode and mode not in [ "min" , "max" ] : raise ValueError ( "If set, `mode` has to be one of [min, max]" ) if mode and not metric : raise ValueError ( "If a `mode` is passed to `ExperimentAnalysis.dataframe()," " you'll also have to pass a `metric`!" ) rows = self . _retrieve_rows ( metric = metric , mode = mode ) all_configs = self . get_all_configs ( prefix = True ) for path , config in all_configs . items ( ) : if path in rows : rows [ path ] . update ( config ) rows [ path ] . update ( logdir = path ) return pd . DataFrame ( list ( rows . values ( ) ) )
Returns a pandas.DataFrame object constructed from the trials.
Returns a pandas . DataFrame object constructed from the trials .
"""Returns a pandas.DataFrame object constructed from the trials.

        This function will look through all observed results of each trial
        and return the one corresponding to the passed ``metric`` and
        ``mode``: If ``mode=min``, it returns the result with the lowest
        *ever* observed ``metric`` for this trial (this is not necessarily
        the last)! For ``mode=max``, it's the highest, respectively. If
        ``metric=None`` or ``mode=None``, the last result will be returned.

        Args:
            metric: Key for trial info to order on. If None, uses last result.
            mode: One of [None, "min", "max"].

        Returns:
            pd.DataFrame: Constructed from a result dict of each trial.
        """ # Do not validate metric/mode here or set from default metric/mode! # Otherwise we will get confusing results as the lowest ever observed # result may not be the last result.
Constructed from a result dict of each trial.
Constructed from a result dict of each trial .
pd.DataFrame
self
metric
Optional[str]
Key for trial info to order on. If None, uses last result.
Key for trial info to order on . If None uses last result .
mode
Optional[str]
None