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test_distributionally_robust.py
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test_distributionally_robust.py
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from distributionally_robust_data import OnlineCressieRead
from math import exp, pi
import numpy as np
def test_recompute_duals_lower():
ocrl = OnlineCressieRead(alpha=0.05, tau=0.999)
ws = np.random.RandomState(seed=42).exponential(size=10)
rs = np.random.RandomState(seed=2112).random_sample(size=10)
duals = []
bounds = []
ws_rs = [
(0.4692680899768591, 0.08779271803562538),
(3.010121430917521, 0.1488852932982503),
(1.3167456935454493, 0.5579699034039329),
(0.9129425537759532, 0.6202896863254631),
(0.16962487046234628, 0.7284609393916186),
(0.16959629191460518, 0.10028857734263497),
(0.059838768608680676, 0.5165390922355259),
(2.0112308644799395, 0.4596272470443479),
(0.9190821536272645, 0.4352012556681023),
(1.2312500617045903, 0.40365563207132593),
]
np.testing.assert_almost_equal(list(zip(ws, rs)), ws_rs, 5)
for w, r in zip(ws, rs):
ocrl.update(1, w, r)
ocrl.recomputeduals()
if ocrl.duals[1][0] is None:
duals.append((True, 0, 0, 0, 0))
else:
bounds.append(ocrl.duals[0][0])
duals.append(
(
False,
ocrl.duals[1][0]["kappastar"],
ocrl.duals[1][0]["gammastar"],
ocrl.duals[1][0]["betastar"],
ocrl.n,
)
)
exp_bounds = [
0.09662899139580064,
0.04094541671274077,
0.12209962713632538,
0.1418901366388003,
0.14012602622199424,
0.10155119256007322,
0.1846416198342555,
0.20562281337616062,
0.23301703596172654,
]
np.testing.assert_almost_equal(exp_bounds, bounds, 5)
exp_duals = [
(True, 0, 0, 0, 0),
(False, 0.186284935714629, -0.5242563567278763, 0, 1.999),
(
False,
0.24176630719751424,
-0.3939735949427358,
-0.1283677781597634,
2.997001,
),
(
False,
0.2789701026811336,
-0.5061803928309371,
-0.11471449055314126,
3.994003999,
),
(
False,
0.28140131203664326,
-0.43475483491188227,
-0.16912405473103076,
4.9900099950009995,
),
(
False,
0.29153800750906095,
-0.3748233156965521,
-0.22291421513333443,
5.985019985005999,
),
(False, 0.37075115114133017, -0.7039218308392182, 0, 6.979034965020992),
(False, 0.563270986745603, -0.9948022925290081, 0, 7.972055930055971),
(False, 0.5602916549924593, -0.9880343238436395, 0, 8.964083874125915),
(False, 0.5684515391242324, -1.0040272155608332, 0, 9.95511979025179),
]
np.testing.assert_almost_equal(duals, exp_duals, 5)
def test_recompute_duals_upper():
ocrl = OnlineCressieRead(alpha=0.05, tau=0.999)
ws = np.random.RandomState(seed=42).exponential(size=10)
rs = np.random.RandomState(seed=2112).random_sample(size=10)
duals = []
bounds = []
for w, r in zip(ws, rs):
ocrl.update(1, w, r)
ocrl.recomputeduals(is_upper=True)
if ocrl.duals[1][0] is None:
duals.append((True, 0, 0, 0, 0))
else:
bounds.append(ocrl.duals[0][0])
duals.append(
(
False,
ocrl.duals[1][0]["kappastar"],
ocrl.duals[1][0]["gammastar"],
ocrl.duals[1][0]["betastar"],
ocrl.n,
)
)
exp_bounds = [
0.28128352388284217,
0.5056614942571485,
0.6220782066057806,
0.75416164144364,
0.8175722577850846,
0.944281011930322,
0.7625855039970183,
0.7386659400005198,
0.69289830401895,
]
np.testing.assert_almost_equal(exp_bounds, bounds, 5)
def test_qlb():
ocrl = OnlineCressieRead(alpha=0.05, tau=0.999)
ws = np.random.RandomState(seed=42).exponential(size=10)
rs = np.random.RandomState(seed=2112).random_sample(size=10)
qlbs = []
ws_rs = [
(0.4692680899768591, 0.08779271803562538),
(3.010121430917521, 0.1488852932982503),
(1.3167456935454493, 0.5579699034039329),
(0.9129425537759532, 0.6202896863254631),
(0.16962487046234628, 0.7284609393916186),
(0.16959629191460518, 0.10028857734263497),
(0.059838768608680676, 0.5165390922355259),
(2.0112308644799395, 0.4596272470443479),
(0.9190821536272645, 0.4352012556681023),
(1.2312500617045903, 0.40365563207132593),
]
np.testing.assert_almost_equal(list(zip(ws, rs)), ws_rs, 5)
for w, r in zip(ws, rs):
ocrl.update(1, w, r)
qlbs.append(ocrl.qlb(w, r))
exp_qlbs = [
1,
0.13620517641052662,
-0.17768396518176874,
0.03202698335276157,
0.20163624787093867,
0.19427440609482105,
0.22750472815940542,
0.01392757858090217,
0.10533233309112934,
0.08141788541188416,
]
np.testing.assert_almost_equal(qlbs, exp_qlbs, 5)