tfma.metrics.ObjectDetectionPrecision

Computes the precision of the predictions with respect to the labels.

Inherits From: Precision, Metric

tfma.metrics.ObjectDetectionPrecision(
    thresholds: Optional[Union[float, List[float]]] = 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
)

The metric uses true positives and false positives to compute precision by dividing the true positives by the sum of true positives and false positives.

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

thresholds (Optional) A float value or a python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value. The default is to calculate precision with thresholds=0.5.
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

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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

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get_config() -> Dict[str, Any]

Returns serializable config.

result

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result(
    tp: float, tn: float, fp: float, fn: float
) -> float

Function for computing metric value from TP, TN, FP, FN values.