def __init__(self, metric1, metric2, granularity_sigfigs=3, **kwargs):
        """
        Accepts a Metric object and evaluates it at each fold.

        Parameters:
        Thresholds: [Float], default: None
            Thresholds for reporting the two metrics. If not set, thresholds
            are model predictions from all runs computed on the fly.

        lower_quantile, upper_quantile: Float, default 0.05, 0.095
            Quantiles to be used for reporting metrics.
        """
        Reporter.__init__(self, **kwargs)
        if (not isinstance(metric1, metrics.ThresholdMetric)
                or not isinstance(metric2, metrics.ThresholdMetric)):
            raise TypeError("Requires ThresholdMetric")
        self.metric1 = metric1
        self.metric2 = metric2
        self.results = []
        self.n_cached_curves = 0
        self.sigfigs = granularity_sigfigs
def __init__ ( self , metric1 , metric2 , granularity_sigfigs = 3 , ** kwargs ) : Reporter . __init__ ( self , ** kwargs ) if ( not isinstance ( metric1 , metrics . ThresholdMetric ) or not isinstance ( metric2 , metrics . ThresholdMetric ) ) : raise TypeError ( "Requires ThresholdMetric" ) self . metric1 = metric1 self . metric2 = metric2 self . results = [ ] self . n_cached_curves = 0 self . sigfigs = granularity_sigfigs
Accepts a Metric object and evaluates it at each fold.

        Parameters:
        Thresholds: [Float], default: None
            Thresholds for reporting the two metrics. If not set, thresholds
            are model predictions from all runs computed on the fly.

        lower_quantile, upper_quantile: Float, default 0.05, 0.095
            Quantiles to be used for reporting metrics.