cleanlab--cleanlab
Elías Snorrason
71ba4b3209
Co-authored-by: Jonas Mueller <1390638+jwmueller@users.noreply.github.com>
32 行
976 B
Python
32 行
976 B
Python
import numpy as np
|
|
import pytest
|
|
|
|
from cleanlab.internal.neighbor.metric import decide_default_metric
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"N",
|
|
[2, 10, 50, 100, 101],
|
|
)
|
|
def test_decide_default_metric_for_2d_and_3d_features(N):
|
|
# 2D and 3D features should always use the euclidean metric, disregarding the different implementations.
|
|
for M in [2, 3]:
|
|
X = np.random.rand(N, M)
|
|
metric = decide_default_metric(X)
|
|
if hasattr(metric, "__name__"):
|
|
error_msg = "The metric should be the string 'euclidean' for N > 100."
|
|
assert N <= 100, error_msg
|
|
metric = getattr(metric, "__name__")
|
|
assert metric == "euclidean"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"M",
|
|
[4, 5, 10, 50, 100],
|
|
)
|
|
def test_decide_default_metric_for_high_dimensional_features(M):
|
|
# High-dimensional features should always use the cosine metric.
|
|
X = np.random.rand(100, M)
|
|
metric = decide_default_metric(X)
|
|
assert metric == "cosine"
|