Model evaluation subsets backend

[brimoor]

Change log

Adds support for computing metrics for subsets of a full set of evaluation results via a new use_subset() method.

Example usage

import fiftyone as fo
import fiftyone.zoo as foz
import fiftyone.utils.random as four
from fiftyone import ViewField as F

dataset = foz.load_zoo_dataset("quickstart")
four.random_split(dataset, {"sunny": 0.7, "cloudy": 0.2, "rainy": 0.1})

results = dataset.evaluate_detections(
    "predictions",
    gt_field="ground_truth",
    eval_key="eval",
)

counts = dataset.count_values("ground_truth.detections.label")
classes = sorted(counts, key=counts.get, reverse=True)[:5]

# Full results
results.print_report(classes=classes)

# Sunny samples
with results.use_subset(field="tags", value="sunny"):
    results.print_report(classes=classes)

# Small objects
bbox_area = F("bounding_box")[2] * F("bounding_box")[3]
small_objects = bbox_area <= 0.05
with results.use_subset(expr=small_objects):
    results.print_report(classes=classes)

Full results:

              precision    recall  f1-score   support

      person       0.52      0.94      0.67       716
        kite       0.59      0.88      0.71       140
         car       0.18      0.80      0.29        61
        bird       0.65      0.78      0.71       110
      carrot       0.09      0.74      0.16        47

   micro avg       0.42      0.90      0.57      1074
   macro avg       0.41      0.83      0.51      1074
weighted avg       0.51      0.90      0.64      1074

Sunny samples:

              precision    recall  f1-score   support

      person       1.00      0.94      0.97       472
        kite       1.00      0.88      0.93       122
         car       1.00      0.91      0.95        34
        bird       1.00      0.78      0.88       106
      carrot       1.00      0.72      0.84        43

   micro avg       1.00      0.90      0.95       777
   macro avg       1.00      0.85      0.92       777
weighted avg       1.00      0.90      0.94       777

Small objects:

              precision    recall  f1-score   support

      person       1.00      0.87      0.93       324
        kite       1.00      0.76      0.87        72
         car       1.00      0.79      0.88        56
        bird       1.00      0.52      0.69        46
      carrot       1.00      0.75      0.86        40

   micro avg       1.00      0.81      0.89       538
   macro avg       1.00      0.74      0.84       538
weighted avg       1.00      0.81      0.89       538

Subsets specification

Subset field

subset_field = "tags"
subsets = {v: (subset_field, v) for v in dataset.distinct(subset_field)}
"""
{
    'cloudy': ('tags', 'cloudy'),
    'rainy': ('tags', 'rainy'),
    'sunny': ('tags', 'sunny'),
}
"""

Subset expressions

from fiftyone import ViewField as F

bbox_area = F("bounding_box")[2] * F("bounding_box")[3]
subsets = {
    "Small objects": bbox_area <= 0.05,
    "Medium objects": (0.05 <= bbox_area) & (bbox_area <= 0.5),
    "Large objects": bbox_area > 0.5,
}

Subsets analysis

def analyze_subsets(results, subsets, classes=None):
    subset_reports = {}
    subset_metrics = {}

    for subset_name, subset_def in subsets.items():
        if isinstance(subset_def, tuple):
            field, value = subset_def
            kwargs = dict(field=field, value=value)
        else:
            kwargs = dict(expr=subset_def)

        with results.use_subset(**kwargs):
            subset_reports[subset_name] = results.report(classes=classes)
            subset_metrics[subset_name] = results.metrics(classes=classes)

    return subset_reports, subset_metrics

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[brimoor]

This should wire up view callbacks for the ME panel, assuming that:

• A subset_name parameter is set that encodes the name of the subset (bar) that the user clicked on
• A subsets dict is available in panel state that encodes the user's current subsets definition, using the structure described in the Subsets specification section of the PR description