tfma.metrics.MeanSquaredLogarithmicError

Calculates the mean of squared logarithmic error.

Inherits From: Metric

tfma.metrics.MeanSquaredLogarithmicError(
    name: str = MEAN_SQUARED_LOGARITHMIC_ERROR_NAME
)

Formula: error = L2_norm(log(label + 1) - log(prediction + 1))**2 Note: log of an array will be elementwise, i.e. log([x1, x2]) = [log(x1), log(x2)]

The metric computes the mean of squared logarithmic error (square of L2 norm) between labels and predictions. The labels and predictions could be arrays of arbitrary dimensions. Their dimension should match.

name The name of the metric.

compute_confidence_interval Whether to compute confidence intervals for this metric.

Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.

Methods

computations

View source

computations(
    eval_config: Optional[tfma.EvalConfig] = None,
    schema: Optional[schema_pb2.Schema] = None,
    model_names: Optional[List[str]] = None,
    output_names: Optional[List[str]] = None,
    sub_keys: Optional[List[Optional[SubKey]]] = None,
    aggregation_type: Optional[AggregationType] = None,
    class_weights: Optional[Dict[int, float]] = None,
    example_weighted: bool = False,
    query_key: Optional[str] = None
) -> tfma.metrics.MetricComputations

Creates computations associated with metric.

from_config

View source

@classmethod
from_config(
    config: Dict[str, Any]
) -> 'Metric'

get_config

View source

get_config() -> Dict[str, Any]

Returns serializable config.