/cleanlab/internal/constants.py

FLOATING_POINT_COMPARISON = 1e-6  # floating point comparison for fuzzy equals
CLIPPING_LOWER_BOUND = 1e-6  # lower-bound clipping threshold for expected behavior
CONFIDENT_THRESHOLDS_LOWER_BOUND = (
    2 * FLOATING_POINT_COMPARISON
)  # lower bound imposed to clip confident thresholds from below, has to be larger than floating point comparison
TINY_VALUE = 1e-100  # very tiny value for clipping


# Object Detection Constants
EUC_FACTOR = 0.1  # Factor to control magnitude of euclidian distance. Increasing the factor makes the distances between two objects go to zero more rapidly.
MAX_ALLOWED_BOX_PRUNE = 0.97  # This is max allowed percent of boxes that are pruned before a warning is thrown given a specific threshold. Pruning too many boxes negatively affects performance.
IOU_THRESHOLD = (
    0.5  # Threshold for considering the predicted box and annotated box to be overlapping
)
EPSILON = 1e-6  # Small value to prevent division by zero

ALPHA = 0.9  # Param for objectlab, weight between IoU and distance when considering similarity matrix. High alpha means considering IoU more strongly over distance
LOW_PROBABILITY_THRESHOLD = 0.5  # Param for get_label_quality_score, lowest predicted class probability threshold allowed when considering predicted boxes to identify badly located label boxes.
HIGH_PROBABILITY_THRESHOLD = 0.95  # Param for objectlab, high probability threshold for considering predicted boxes to identify overlooked and swapped label boxes
TEMPERATURE = 0.1  # Param for objectlab, temperature of the softmin function used to pool the per-box quality scores for an error subtype across all boxes into a single subtype score for the image. With a lower temperature, softmin pooling acts more like minimum pooling, alternatively it acts more like mean pooling with high temperature.
LABEL_OVERLAP_THRESHOLD = 0.95  # Param for objectlab, minimum IoU threshold for deciding when two boxes overlap used for deciding which objects have multiple conflicting annotations.

OVERLOOKED_THRESHOLD_FACTOR = 0.8  # Param for find_label_issues. Per-box label quality score threshold scale factor to determine max score for a box to be considered an overlooked issue
BADLOC_THRESHOLD_FACTOR = 0.8  # Param for find_label_issues. Per-box label quality score threshold scale factor to determine max score for a box to be considered a bad location issue
SWAP_THRESHOLD_FACTOR = 0.8  # Param for find_label_issues. Per-box label quality score threshold scale factor to determine max score for a box to be considered a swap issue
AP_SCALE_FACTOR = 0.25  #  Param for find_label_issues. Scale factor for per-class precision to determine is_issue.

CUSTOM_SCORE_WEIGHT_OVERLOOKED = (
    1 / 3
)  # Param for get_label_quality_score, weight to determine how much to value overlooked scores over other subtypes when deciding the overall label quality score for an image.
CUSTOM_SCORE_WEIGHT_BADLOC = (
    1 / 3
)  # Param for get_label_quality_score, weight to determine how much to value badloc scores over other subtypes when deciding issues
CUSTOM_SCORE_WEIGHT_SWAP = (
    1 / 3
)  # Param for get_label_quality_score, weight to determine how much to value swap scores over other subtypes when deciding issues

MAX_CLASS_TO_SHOW = 10  # Number of classes to show in legend during the visualize method. Classes over max_class_to_show are cut off.