65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
from __future__ import annotations
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from contextlib import ExitStack as DoesNotRaise
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import numpy as np
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import pytest
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from supervision.classification.core import Classifications
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@pytest.mark.parametrize(
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("class_id", "confidence", "k", "expected_result", "exception"),
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[
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(
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np.array([0, 1, 2, 3, 4]),
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np.array([0.1, 0.2, 0.9, 0.4, 0.5]),
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5,
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(np.array([2, 4, 3, 1, 0]), np.array([0.9, 0.5, 0.4, 0.2, 0.1])),
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DoesNotRaise(),
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), # class_id with 5 numbers and 5 confidences
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(
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np.array([5, 1, 2, 3, 4]),
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np.array([0.1, 0.2, 0.9, 0.4, 0.5]),
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1,
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(np.array([2]), np.array([0.9])),
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DoesNotRaise(),
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), # class_id with 5 numbers and 5 confidences, retrieve where k = 1
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(
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np.array([4, 1, 2, 3, 6, 5]),
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np.array([0.8, 0.2, 0.9, 0.4, 0.5, 0.1]),
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2,
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(np.array([2, 4]), np.array([0.9, 0.8])),
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DoesNotRaise(),
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), # class_id with 5 numbers and 5 confidences, retrieve where k = 3
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(
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np.array([0, 1, 2, 3, 4]),
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np.array([]),
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5,
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None,
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pytest.raises(ValueError),
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), # class_id with 5 numbers and 0 confidences
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(
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[0, 1, 2, 3, 4],
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[0.1, 0.2, 0.3, 0.4],
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5,
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None,
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pytest.raises(ValueError),
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), # class_id with 5 numbers and 4 confidences
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],
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)
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def test_top_k(
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class_id: np.ndarray,
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confidence: np.ndarray | None,
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k: int,
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expected_result: tuple[np.ndarray, np.ndarray] | None,
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exception: Exception,
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) -> None:
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with exception:
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result = Classifications(
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class_id=np.array(class_id), confidence=np.array(confidence)
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).get_top_k(k)
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assert np.array_equal(result[0], expected_result[0])
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assert np.array_equal(result[1], expected_result[1])
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