add unit tests

tests/test_computeBayesTest.py

@@ -0,0 +1,21 @@
+import pytest
+from bayes import computeBayesTest
+
+def test_monotonicity_rates_v1():
+    _,P = computeBayesTest([0.2,0.1],1000)
+    assert P.loc["A","B"] > 0.5
+
+def test_monotonicity_rates_v2():
+    _,P1 = computeBayesTest([0.9,0.1],1000)
+    _,P2 = computeBayesTest([0.2,0.1],1000)
+    assert P1.loc["A","B"] >= P2.loc["A","B"]
+
+def test_monotonicity_volumes():
+    _,P1 = computeBayesTest([0.2,0.1],1000)
+    _,P2 = computeBayesTest([0.2,0.1],100)
+    assert P1.loc["A","B"] >= P2.loc["A","B"]
+
+def test_monotonicity_confidence():
+    C1,_ = computeBayesTest([0.2,0.1],1000,credible_interval_prob=80.)
+    C2,_ = computeBayesTest([0.2,0.1],1000,credible_interval_prob=90.)
+    assert C2.loc["A","B"][1]-C2.loc["A","B"][0] >= C1.loc["A","B"][1]-C1.loc["A","B"][0]

tests/test_computeBayesTestSampleSize.py

@@ -0,0 +1,40 @@
+import pytest
+import numpy as np
+import re
+from bayes import computeBayesTest,computeBayesTestSampleSize
+
+def empiricalRateSuccess(n,p1,p2,n_mc):
+    rate_list = np.random.binomial(n/2.,p2,n_mc)/(n/2.)
+    pval_list = [np.nan for i in rate_list]
+    for count,rate in enumerate(rate_list):
+        pval_list[count] = computeBayesTest([p1,rate],n)[1].loc["B","A"]
+    return(np.mean(np.array(pval_list)>0.8))
+
+def test_size_1():
+    p1,p2 = 0.1,0.12
+    n_mc = 100
+    n = computeBayesTestSampleSize([p1,p2],accuracy=1)
+    rate_success = empiricalRateSuccess(n,p1,p2,n_mc)
+    assert ((rate_success<=0.83)and(rate_success>=0.77)),f"Rate success is {rate_success} instead of\
+    0.8, with sample size of {n}"
+
+def test_size_2():
+    p1,p2 = 0.5,0.8
+    n_mc = 100
+    n = computeBayesTestSampleSize([p1,p2],accuracy=1)
+    rate_success = empiricalRateSuccess(n,p1,p2,n_mc)
+    assert ((rate_success<=0.83)and(rate_success>=0.77)),f"""Rate success is {rate_success} instead of """ + \
+    f"""0.8, with sample size of {n}"""
+
+def test_error_large_diff(capfd):
+    p1,p2 = 0.1,0.9
+    computeBayesTestSampleSize([p1,p2],accuracy=1)
+    out, _ = capfd.readouterr()
+    assert bool(re.match("""The values in the explored size range are all large enough to """ + \
+        """confirm that the positive rates""",out))
+
+def test_error_small_diff(capfd):
+    p1,p2 = 0.1,0.10001
+    computeBayesTestSampleSize([p1,p2],accuracy=1)
+    out, _ = capfd.readouterr()
+    assert bool(re.match("""The values in the explored size range are too small to confirm """,out))