valor-lite 0.36.4

Evaluate machine learning models.

valor-lite: Fast, local machine learning evaluation.

valor-lite is a lightweight, numpy-based library designed for fast and seamless evaluation of machine learning models. It is optimized for environments where quick, responsive evaluations are essential, whether as part of a larger service or embedded within user-facing tools.

valor-lite is maintained by Striveworks, a cutting-edge MLOps company based in Austin, Texas. If you'd like to learn more or have questions, we invite you to connect with us on Slack or explore our GitHub repository.

For additional details, be sure to check out our user documentation. We're excited to support you in making the most of Valor!

Usage

Classification

from valor_lite.classification import DataLoader, Classification, MetricType

classifications = [
    Classification(
        uid="uid0",
        groundtruth="dog",
        predictions=["dog", "cat", "bird"],
        scores=[0.75, 0.2, 0.05],
    ),
    Classification(
        uid="uid1",
        groundtruth="cat",
        predictions=["dog", "cat", "bird"],
        scores=[0.41, 0.39, 0.1],
    ),
]

loader = DataLoader()
loader.add_data(classifications)
evaluator = loader.finalize()

metrics = evaluator.evaluate()

assert metrics[MetricType.Precision][0].to_dict() == {
    'type': 'Precision',
    'value': [0.5],
    'parameters': {
        'score_thresholds': [0.0],
        'hardmax': True,
        'label': 'dog'
    }
}

Object Detection

from valor_lite.object_detection import DataLoader, Detection, BoundingBox, MetricType

detections = [
    Detection(
        uid="uid0",
        groundtruths=[
            BoundingBox(
                xmin=0, xmax=10,
                ymin=0, ymax=10,
                labels=["dog"]
            ),
            BoundingBox(
                xmin=20, xmax=30,
                ymin=20, ymax=30,
                labels=["cat"]
            ),
        ],
        predictions=[
            BoundingBox(
                xmin=1, xmax=11,
                ymin=1, ymax=11,
                labels=["dog", "cat", "bird"],
                scores=[0.85, 0.1, 0.05]
            ),
            BoundingBox(
                xmin=21, xmax=31,
                ymin=21, ymax=31,
                labels=["dog", "cat", "bird"],
                scores=[0.34, 0.33, 0.33]
            ),
        ],
    ),
]

loader = DataLoader()
loader.add_bounding_boxes(detections)
evaluator = loader.finalize()

metrics = evaluator.evaluate()

assert metrics[MetricType.Precision][0].to_dict() == {
    'type': 'Precision',
    'value': 0.5,
    'parameters': {
        'iou_threshold': 0.5,
        'score_threshold': 0.5,
        'label': 'dog'
    }
}

Semantic Segmentation

import numpy as np
from valor_lite.semantic_segmentation import DataLoader, Segmentation, Bitmask, MetricType

segmentations = [
    Segmentation(
        uid="uid0",
        groundtruths=[
            Bitmask(
                mask=np.random.randint(2, size=(10,10), dtype=np.bool_),
                label="sky",
            ),
            Bitmask(
                mask=np.random.randint(2, size=(10,10), dtype=np.bool_),
                label="ground",
            )
        ],
        predictions=[
            Bitmask(
                mask=np.random.randint(2, size=(10,10), dtype=np.bool_),
                label="sky",
            ),
            Bitmask(
                mask=np.random.randint(2, size=(10,10), dtype=np.bool_),
                label="ground",
            )
        ]
    ),
]

loader = DataLoader()
loader.add_data(segmentations)
evaluator = loader.finalize()

print(metrics[MetricType.Precision][0])