def test_var():
assert StreamHist().update(1).var() is None
h = StreamHist()
for p in [1, 1, 2, 3, 4, 5, 6, 6]:
h.update(p)
assert h.var() == 3.75
h = StreamHist()
for p in make_normal(10000):
h.update(p)
assert about(h.var(), 1, 0.05)
def test_var():
assert StreamHist().update(1).var() is None
h = StreamHist()
for p in [1, 1, 2, 3, 4, 5, 6, 6]:
h.update(p)
assert h.var() == 3.75
h = StreamHist()
for p in make_normal(10000):
h.update(p)
assert about(h.var(), 1, 0.05)
def test_freeze():
points = 100000
h = StreamHist(freeze=500)
for p in make_normal(points):
h.update(p)
assert about(h.sum(0), points / 2.0, points / 50.0)
assert about(h.median(), 0, 0.05)
assert about(h.mean(), 0, 0.05)
assert about(h.var(), 1, 0.05)
def test_freeze():
points = 100000
h = StreamHist(freeze=500)
for p in make_normal(points):
h.update(p)
assert about(h.sum(0), points/2.0, points/50.0)
assert about(h.median(), 0, 0.05)
assert about(h.mean(), 0, 0.05)
assert about(h.var(), 1, 0.05)
def test_histogram_exact():
"""A StreamHist which is not at capacity matches numpy statistics"""
max_bins = 50
points = [random.expovariate(1 / 5) for _ in range(max_bins)]
h = StreamHist(max_bins)
h.update(points)
q = [i / 100 for i in range(101)]
import numpy as np
assert h.quantiles(*q) == approx(np.quantile(points, q))
assert h.mean() == approx(np.mean(points))
assert h.var() == approx(np.var(points))
assert h.min() == min(points)
assert h.max() == max(points)
assert h.count() == max_bins
def test_histogram_approx(max_bins, num_points, expected_error):
"""Test accuracy of StreamHist over capacity, especially quantiles."""
points = [random.expovariate(1 / 5) for _ in range(num_points)]
h = StreamHist(max_bins)
h.update(points)
import numpy as np
q = [i / 100 for i in range(101)]
err_sum = 0 # avg percent error across samples
for p, b, b_np, b_np_min, b_np_max in zip(
q, h.quantiles(*q), np.quantile(points, q),
np.quantile(points, [0] * 7 + q),
np.quantile(points, q[7:] + [1] * 7)):
err_denom = b_np_max - b_np_min
err_sum += abs(b - b_np) / err_denom
assert err_sum <= expected_error
assert h.mean() == approx(np.mean(points))
assert h.var() == approx(np.var(points), rel=.05)
assert h.min() == min(points)
assert h.max() == max(points)
assert h.count() == num_points
