def test_groups():
y = numpy.empty(shape=7,
dtype=[("event", numpy.bool_), ("time", numpy.float_)])
y["time"] = numpy.arange(1, 8)
y["event"] = True
group = numpy.ones(7)
with pytest.raises(ValueError,
match="At least two groups must be specified, "
"but only one was provided."):
compare_survival(y, group)
def _get_best_split(self, X, y):
min_leaf = self.min_leaf
best_val = TREE_UNDEFINED
best_feat = TREE_UNDEFINED
best_stat = -numpy.infty
if y[y.dtype.names[0]].sum() == 0:
return best_val, best_feat, best_stat
for j in range(X.shape[1]):
vals = X[:, j]
values = numpy.unique(vals)
if len(values) < 2:
continue
for i, v in enumerate(values[:-1]):
t = (v + values[i + 1]) * 0.5
groups = (vals <= t).astype(int)
if groups.sum() >= min_leaf and (X.shape[0] -
groups.sum()) >= min_leaf:
s, _ = compare_survival(y, groups)
if s > best_stat:
best_feat = j
best_val = t
best_stat = s
return best_val, best_feat, best_stat
def test_logrank_three():
y = numpy.empty(51, dtype=[("status", bool), ("time", float)])
y["time"] = [
88, 62, 81, 64, 19, 35, 49, 82, 57, 6, 97, 52, 4, 90, 16, 92, 4, 59,
90, 71, 39, 94, 68, 77, 67, 15, 67, 7, 32, 14, 39, 26, 24, 2, 4, 95, 2,
12, 94, 64, 68, 16, 99, 6, 57, 81, 53, 33, 31, 91, 91
]
y["status"] = [
False, False, True, False, False, False, True, True, True, True, True,
False, False, False, False, False, False, False, False, False, False,
False, False, False, False, True, False, False, True, False, True,
True, False, False, False, True, False, False, True, False, False,
True, False, True, False, True, False, False, True, False, False
]
x = numpy.array([
1, 13, 1, 1, 13, 4, 1, 4, 4, 4, 1, 4, 13, 4, 4, 1, 13, 4, 4, 4, 4, 13,
4, 13, 1, 4, 4, 13, 13, 4, 13, 13, 1, 4, 4, 4, 13, 4, 13, 4, 13, 13,
13, 1, 13, 13, 1, 13, 13, 13, 13
])
chisq, pval = compare_survival(y, x)
expected_chisq = 0.0398155305699465
expected_pval = 0.980289085821579
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def test_logrank_two_2():
y = numpy.empty(10, dtype=[("status", bool), ("time", float)])
y["time"] = [12, 1, 6, 9, 7, 4, 13, 5, 2, 3]
y["status"] = [
False, False, True, False, False, False, True, False, True, True
]
x = numpy.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
chisq, pval = compare_survival(y, x)
expected_chisq = 0.843638413831986
expected_pval = 0.358358251735077
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def test_logrank_two_1():
y = numpy.empty(10, dtype=[("status", bool), ("time", float)])
y["time"] = [12, 1, 6, 9, 7, 4, 13, 5, 2, 3]
y["status"] = [
False, True, True, False, False, False, True, False, True, True
]
x = numpy.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
chisq, pval = compare_survival(y, x)
expected_chisq = 0.0658861965695617
expected_pval = 0.797423444741849
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def test_logrank_two_6():
y = numpy.empty(10, dtype=[("status", bool), ("time", float)])
y["time"] = [12, 12, 2, 2, 7, 4, 1, 5, 2, 5]
y["status"] = [
True, True, False, False, False, False, False, True, False, True
]
x = numpy.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
chisq, pval = compare_survival(y, x)
expected_chisq = 4.
expected_pval = 0.0455002638963585
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def test_logrank_two_4():
y = numpy.empty(10, dtype=[("status", bool), ("time", float)])
y["time"] = [12, 1, 2, 2, 7, 4, 1, 5, 2, 5]
y["status"] = [
False, False, True, False, False, False, True, False, True, True
]
x = numpy.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
chisq, pval = compare_survival(y, x)
expected_chisq = 1.07692307692308
expected_pval = 0.299386905803601
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def test_logrank_veterans():
X, y = load_veterans_lung_cancer()
chisq, pval, stats, covar = compare_survival(y,
X.loc[:, "Celltype"],
return_stats=True)
expected_stats = pandas.DataFrame(
columns=["counts", "observed", "expected", "statistic"],
index=["adeno", "large", "smallcell", "squamous"])
expected_stats.index.name = "group"
expected_stats["counts"] = numpy.array([27, 27, 48, 35], dtype=numpy.intp)
expected_stats["observed"] = numpy.array([26, 26, 45, 31],
dtype=numpy.int_)
expected_stats["expected"] = [
15.6937646143605, 34.5494783863493, 30.1020793268148, 47.6546776724754
]
expected_stats[
"statistic"] = expected_stats["observed"] - expected_stats["expected"]
assert_frame_equal(stats, expected_stats)
expected_var = numpy.array([[
12.9661700605014, -4.07011754397142, -4.40872930298506,
-4.48732321354496
],
[
-4.07011754397142, 24.1990352938484,
-7.81168661717217, -12.3172311327048
],
[
-4.40872930298506, -7.81168661717217,
21.7542679406138, -9.53385202045655
],
[
-4.48732321354496, -12.3172311327048,
-9.53385202045655, 26.3384063667063
]])
assert_almost_equal(covar, expected_var)
expected_chisq = 25.4037003457854
expected_pval = 1.27124593900609e-05
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def test_logrank_rossi(rossi):
chisq, pval, stats, covar = compare_survival(rossi.y,
rossi.x.loc[:, "race"],
return_stats=True)
expected_counts = numpy.array([53, 379])
expected_obs = numpy.array([12, 102])
expected_exp = numpy.array([14.709347908949, 99.2906520910509])
expected_var = numpy.array([[12.7417883289863, -12.7417883289863],
[-12.7417883289863, 12.7417883289863]])
assert_almost_equal(stats["counts"], expected_counts)
assert_almost_equal(stats["observed"], expected_obs)
assert_almost_equal(stats["expected"], expected_exp)
assert_almost_equal(stats["statistic"], expected_obs - expected_exp)
assert_almost_equal(covar, expected_var)
expected_chisq = 0.576101713683918
expected_pval = 0.447844394252168
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def test_logrank_four():
y = numpy.empty(40, dtype=[("status", bool), ("time", float)])
y["time"] = [
95, 54, 94, 72, 52, 68, 53, 53, 21, 69, 58, 67, 65, 22, 95, 78, 86, 18,
35, 43, 72, 88, 24, 53, 3, 41, 36, 3, 55, 89, 53, 8, 13, 94, 12, 64, 9,
56, 8, 7
]
y["status"] = [
False, False, True, False, False, True, False, True, False, True,
False, True, False, True, True, True, True, True, False, True, False,
True, False, True, True, False, False, True, True, False, True, False,
True, True, False, False, True, True, True, False
]
x = numpy.array([
3, 3, 2, -1, 3, -1, 2, 0, -1, 2, 2, -1, -1, 0, 2, 2, -1, -1, -1, 3, -1,
-1, 2, -1, 3, 2, -1, 2, 0, 2, 2, 2, 2, 3, -1, 2, -1, 2, 2, 2
])
chisq, pval = compare_survival(y, x)
expected_chisq = 5.25844160740909
expected_pval = 0.153821897350625
assert round(abs(chisq - expected_chisq), 6) == 0
assert round(abs(pval - expected_pval), 6) == 0
def logrank_test(y, group_indicator, return_stats=False):
chisq, pval = compare_survival(y, group_indicator, return_stats=False)
return pval
