Metadata-Version: 2.1
Name: valor-lite
Version: 0.33.9
Summary: Compute valor metrics locally.
License: MIT License
        
        Copyright (c) 2023 Striveworks
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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Project-URL: homepage, https://www.striveworks.com
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: Pillow>=9.1.0
Requires-Dist: importlib_metadata; python_version < "3.8"
Requires-Dist: tqdm
Requires-Dist: requests
Requires-Dist: numpy
Requires-Dist: shapely
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Requires-Dist: coverage; extra == "test"

# 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](https://striveworks-public.slack.com/join/shared_invite/zt-1a0jx768y-2J1fffN~b4fXYM8GecvOhA#/shared-invite/email) or explore our [GitHub repository](https://github.com/striveworks/valor).

For additional details, be sure to check out our user [documentation](https://striveworks.github.io/valor/). We're excited to support you in making the most of Valor!

## Usage

### Classification

```python
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

```python
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

```python
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])
```
