def test_queries_from_shrivastava_example():
# NOTE: percentiles and rank given by q-digest are
# approximated, thus no sense to compare them to the
# exact values in a test.
qd = QuantileDigest(3, 5)
for i in range(1):
qd.add(0)
for i in range(4):
qd.add(2)
for i in range(6):
qd.add(3)
for i in range(1):
qd.add(4)
for i in range(1):
qd.add(5)
for i in range(1):
qd.add(6)
for i in range(1):
qd.add(7)
qd.compress()
median = qd.quantile_query(0.5)
assert median == 3, "Incorrect approx. median"
rank = qd.inverse_quantile_query(3)
assert rank == 4, "Incorrect approx. rank"
percentile85 = qd.quantile_query(0.85)
assert percentile85 == 7, "Incorrect approx. 85th percentile"
rank = qd.inverse_quantile_query(5)
assert rank == 10, "Incorrect approx. rank"
num_of_values = qd.interval_query(3, 5)
assert num_of_values == 6, "Incorrect approx. number of values in interval"
"""Example how to use QuantileDigest."""
import random
from pdsa.rank.qdigest import QuantileDigest
if __name__ == '__main__':
qd = QuantileDigest(4, 5)
random.seed(42)
for i in range(100):
qd.add(random.randrange(0, 16))
qd.compress()
print(qd)
print("Size in bytes of the q-digest:", qd.sizeof())
print("Total elements in the q-digest:", qd.count())
print("50th percentile (median):", qd.quantile_query(0.5))
print("95th percentile:", qd.quantile_query(0.95))
print("Rank of the element <10>:", qd.inverse_quantile_query(10))
print("Number of elements in [4, 9]:", qd.interval_query(4, 9))