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import random
import numpy as np
from llm_studio.app_utils.sections.histogram_card import compute_quantile_df
def test_quantiles_are_computed_correctly() -> None:
for _ in range(5):
data: list[int] = np.random.randint(0, 1000, 100_000).tolist()
a = round(random.uniform(0.05, 0.95), 2)
b = round(random.uniform(a, 0.95), 2)
a, b = min(a, b), max(a, b)
df_quantile = compute_quantile_df(data, a, b)
first = df_quantile[
df_quantile["data_type"] == f"first {int(a * 100)}% quantile"
]
last = df_quantile[
df_quantile["data_type"] == f"last {100 - int(b * 100)}% quantile"
]
sorted_data = sorted(data)
# use -1 and +1 to account for rounding errors
expected_first_quantile_range = sorted_data[
int(len(sorted_data) * a) - 1 : int(len(sorted_data) * a) + 1
]
expected_last_quantile_range = sorted_data[
-int(len(sorted_data) * (1 - b)) - 1 : -int(len(sorted_data) * (1 - b)) + 1
]
assert first["length"].values[-1] in expected_first_quantile_range
assert last["length"].values[0] in expected_last_quantile_range
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