tfma.metrics.ObjectDetectionThresholdAtRecall

Computes maximum threshold where recall is >= specified value.

Inherits From: Metric

tfma.metrics.ObjectDetectionThresholdAtRecall(
    recall: Union[float, List[float]],
    thresholds: Optional[List[float]] = None,
    num_thresholds: Optional[int] = None,
    name: Optional[str] = None,
    iou_threshold: Optional[float] = None,
    class_id: Optional[int] = None,
    class_weight: Optional[float] = None,
    area_range: Optional[Tuple[float, float]] = None,
    max_num_detections: Optional[int] = None,
    labels_to_stack: Optional[List[str]] = None,
    predictions_to_stack: Optional[List[str]] = None,
    num_detections_key: Optional[str] = None,
    allow_missing_key: bool = False
)

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

recall A scalar or a list of scalar values in range [0, 1].
thresholds (Optional) Thresholds to use for calculating the matrices. Use one of either thresholds or num_thresholds.
num_thresholds (Optional) Defaults to 1000. The number of thresholds to use for matching the given recall.
name (Optional) string name of the metric instance.
iou_threshold (Optional) Thresholds for a detection and ground truth pair with specific iou to be considered as a match. Default to 0.5
class_id (Optional) The class id for calculating metrics.
class_weight (Optional) The weight associated with the object class id.
area_range (Optional) A tuple (inclusive) representing the area-range for objects to be considered for metrics. Default to (0, inf).
max_num_detections (Optional) The maximum number of detections for a single image. Default to None.
labels_to_stack (Optional) Keys for columns to be stacked as a single numpy array as the labels. It is searched under the key labels, features and transformed features. The desired format is [left bounadary, top boudnary, right boundary, bottom boundary, class id]. e.g. ['xmin', 'ymin', 'xmax', 'ymax', 'class_id']
predictions_to_stack (Optional) Output names for columns to be stacked as a single numpy array as the prediction. It should be the model's output names. The desired format is [left bounadary, top boudnary, right boundary, bottom boundary, class id, confidence score]. e.g. ['xmin', 'ymin', 'xmax', 'ymax', 'class_id', 'scores']
num_detections_key (Optional) An output name in which to find the number of detections to use for evaluation for a given example. It does nothing if predictions_to_stack is not set. The value for this output should be a scalar value or a single-value tensor. The stacked predicitions will be truncated with the specified number of detections.
allow_missing_key (Optional) If true, the preprocessor will return empty array instead of raising errors.

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.