def testShuffledDataframeRelativeToJackknife(self):
# Same as test above, but also testing that reordering the data doesn't
# change results, up to order.
df = pd.DataFrame({
"X": range(11),
"Y": np.concatenate((np.zeros(6), np.ones(5))),
"Z": np.concatenate((np.zeros(3), np.ones(8)))
})
metric = metrics.Distribution("X", ["Z"])
se_method = standard_errors.Jackknife()
output = core.Analyze(df.iloc[np.random.permutation(11)]).relative_to(
comparisons.AbsoluteDifference(
"Y",
0)).with_standard_errors(se_method).calculate(metric).run()
output = (output.reset_index().sort_values(by=["Y", "Z"]).set_index(
["Y", "Z"]))
correct = pd.DataFrame(
np.array([[-0.2, 0.18100283490], [0.2, 0.18100283490]]),
columns=[
"X Distribution Absolute Difference",
"X Distribution Absolute Difference Jackknife SE"
],
index=pd.MultiIndex(levels=[[1.], [0., 1.]],
labels=[[0, 0], [0, 1]],
names=["Y", "Z"]))
correct = (correct.reset_index().sort_values(by=["Y", "Z"]).set_index(
["Y", "Z"]))
self.assertTrue(
all(output.index == correct.index)
and all(output.columns == correct.columns)
and np.all(abs(output.values - correct.values) < 1e-10))
def testRelativeToSplitJackknife(self):
data = pd.DataFrame({
"X": [1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8],
"Y": [1, 1, 1, 2, 2, 2, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3],
"Z": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]
})
metric = metrics.Sum("X")
comparison = comparisons.AbsoluteDifference("Z", 0)
se_method = standard_errors.Jackknife()
output = core.Analyze(data).split_by("Y").relative_to(
comparison).with_standard_errors(se_method).calculate(
metric).run()
rowindex = pd.MultiIndex(levels=[[1, 2, 3], [1]],
labels=[[0, 1, 2], [0, 0, 0]],
names=["Y", "Z"])
correct = pd.DataFrame(
np.array([[-3.0,
np.sqrt(5 * np.var([0, -1, -2, -3, -4, -5]))],
[-3.0, np.sqrt(5 * np.var([3, 2, 1, -8, -7, -6]))],
[-3.0,
np.sqrt(5 * np.var([6, 5, 4, -11, -10, -9]))]]),
columns=("sum(X) Absolute Difference",
"sum(X) Absolute Difference Jackknife SE"),
index=rowindex)
self.assertTrue(output.equals(correct))
def testDataframeRelativeToJackknife(self):
df = pd.DataFrame({
"X": range(11),
"Y": np.concatenate((np.zeros(6), np.ones(5))),
"Z": np.concatenate((np.zeros(3), np.ones(8)))
})
metric = metrics.Distribution("X", ["Z"])
se_method = standard_errors.Jackknife()
output = core.Analyze(df).relative_to(
comparisons.AbsoluteDifference(
"Y",
0)).with_standard_errors(se_method).calculate(metric).run()
correct = pd.DataFrame(
np.array([[-0.2, 0.18100283490], [0.2, 0.18100283490]]),
columns=[
"X Distribution Absolute Difference",
"X Distribution Absolute Difference Jackknife SE"
],
index=pd.MultiIndex(levels=[[1.], [0., 1.]],
labels=[[0, 0], [0, 1]],
names=["Y", "Z"]))
self.assertTrue(
all(output.index == correct.index)
and all(output.columns == correct.columns)
and np.all(abs(output.values - correct.values) < 1e-10))
def testDoubleSEMethodDefinitionRaisesException(self):
data = pd.DataFrame({"X": [1, 2, 3, 4, 5]})
se_method = standard_errors.Jackknife()
with self.assertRaises(ValueError):
core.Analyze(data).with_standard_errors(
se_method).with_standard_errors(se_method)
def testNinetyFiveCIsWithComparison(self):
data = pd.DataFrame({
"X": range(11),
"Y": [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
})
metric = metrics.Sum("X")
comparison = comparisons.AbsoluteDifference("Y", 0)
se_method = standard_errors.Jackknife(confidence=0.95)
output = core.Analyze(data).with_standard_errors(
se_method).relative_to(comparison).calculate(metric).run()
multiplier = scipy.stats.t.ppf(0.975, 10)
correct_mean = 25
correct_buckets = [
15., 16., 17., 18., 19., 25., 26., 27., 28., 29., 30.
]
m = sum(correct_buckets) / len(correct_buckets)
r = sum([(b - m)**2 for b in correct_buckets])
correct_sd = np.sqrt(r * (len(correct_buckets) - 1) /
len(correct_buckets))
correct_lower = correct_mean - multiplier * correct_sd
correct_upper = correct_mean + multiplier * correct_sd
rowindex = pd.Index([1], name="Y")
correct = pd.DataFrame(
{
"sum(X) Absolute Difference": correct_mean,
"sum(X) Absolute Difference Jackknife CI-lower": correct_lower,
"sum(X) Absolute Difference Jackknife CI-upper": correct_upper
},
index=rowindex)
self.assertTrue(output.equals(correct))
def testSingleJackknifeBucketRaisesException(self):
data = pd.DataFrame({"X": [1, 2, 3, 4, 5], "Y": [1, 1, 1, 1, 1]})
metric = metrics.Sum("X")
se_method = standard_errors.Jackknife(unit="Y")
with self.assertRaises(ValueError):
core.Analyze(data).with_standard_errors(se_method).calculate(
metric).run()
def testJackknife(self):
data = pd.DataFrame({"X": range(11)})
metric = metrics.Sum("X")
se_method = standard_errors.Jackknife()
output = core.Analyze(data).with_standard_errors(se_method).calculate(
metric).run()
correct = pd.DataFrame(np.array([[55.0, 10.0]]),
columns=("sum(X)", "sum(X) Jackknife SE"))
self.assertTrue(output.equals(correct))
def testJackknifeBadSample(self):
data = pd.DataFrame({"X": range(22), "Y": ([0] * 11) + ([1] * 11)})
metric = metrics.Sum("X")
se_method = standard_errors.Jackknife()
output = core.Analyze(data).split_by("Y").with_standard_errors(
se_method).calculate(metric).run()
correct = pd.DataFrame(np.array([[55.0, 10.0], [176.0, 10.0]]),
columns=("sum(X)", "sum(X) Jackknife SE"))
correct.index.name = "Y"
self.assertTrue(output.equals(correct))
def testJackknifeRatio(self):
data = pd.DataFrame({"X": [1, 2, 3, 4], "Y": [4, 3, 2, 1]})
metric = metrics.Ratio("X", "Y")
se_method = standard_errors.Jackknife()
output = core.Analyze(data).with_standard_errors(se_method).calculate(
metric).run()
estimates = np.array([9 / 6, 8 / 7, 7 / 8, 6 / 9])
rss = ((estimates - estimates.mean())**2).sum()
se = np.sqrt(rss * 3 / 4)
correct = pd.DataFrame([[1.0, se]],
columns=("X/Y", "X/Y Jackknife SE"))
self.assertTrue(output.equals(correct))
def testSplitJackknife(self):
data = pd.DataFrame({
"X": np.array([range(11) + [5] * 10]).flatten(),
"Y": np.array([[0] * 11 + [1] * 10]).flatten()
})
metric = metrics.Sum("X")
se_method = standard_errors.Jackknife()
output = core.Analyze(data).split_by("Y").with_standard_errors(
se_method).calculate(metric).run()
rowindex = pd.Index([0, 1], name="Y")
correct = pd.DataFrame(np.array([[55.0, 10.0], [50.0, 0.0]]),
columns=("sum(X)", "sum(X) Jackknife SE"),
index=rowindex)
self.assertTrue(output.equals(correct))
def testRelativeToJackknifeSingleComparisonBaselineSecond(self):
data = pd.DataFrame({"X": [1, 2, 3, 4, 5, 6], "Y": [0, 0, 0, 1, 1, 1]})
metric = metrics.Sum("X")
comparison = comparisons.AbsoluteDifference("Y", 1)
se_method = standard_errors.Jackknife()
output = core.Analyze(data).relative_to(
comparison).with_standard_errors(se_method).calculate(
metric).run()
rowindex = pd.Index([0], name="Y")
correct = pd.DataFrame(
np.array([[-9.0, np.sqrt(5 * np.var([12, 11, 10, 5, 4, 3]))]]),
columns=("sum(X) Absolute Difference",
"sum(X) Absolute Difference Jackknife SE"),
index=rowindex)
self.assertTrue(output.equals(correct))
def testFiftyCIs(self):
data = pd.DataFrame({"X": range(11)})
metric = metrics.Sum("X")
se_method = standard_errors.Jackknife(confidence=0.50)
output = core.Analyze(data).with_standard_errors(se_method).calculate(
metric).run()
multiplier = scipy.stats.t.ppf(0.75, 10)
correct_sd = 10.0
correct_mean = 55.0
correct_lower = correct_mean - multiplier * correct_sd
correct_upper = correct_mean + multiplier * correct_sd
correct = pd.DataFrame(np.array(
[[correct_mean, correct_lower, correct_upper]]),
columns=("sum(X)", "sum(X) Jackknife CI-lower",
"sum(X) Jackknife CI-upper"))
self.assertTrue(output.equals(correct))
def testDataframeJackknife(self):
df = pd.DataFrame({
"X": range(11),
"Y": np.concatenate((np.zeros(6), np.ones(5))),
"Z": np.concatenate((np.zeros(3), np.ones(8)))
})
metric = metrics.Distribution("X", ["Z"])
se_method = standard_errors.Jackknife("Y")
output = core.Analyze(df).with_standard_errors(se_method).calculate(
metric).run()
correct = pd.DataFrame(
np.array([[3 / 55.,
np.sqrt(((3 / 15. - 0.1)**2 + 0.1**2) / 2.)],
[52 / 55.,
np.sqrt(((12 / 15. - 0.9)**2 + 0.1**2) / 2.)]]),
columns=("X Distribution", "X Distribution Jackknife SE"),
index=pd.Index([0., 1.], name="Z"))
self.assertTrue(
all(output.index == correct.index)
and all(output.columns == correct.columns)
and np.all(abs(output.values - correct.values) < 1e-10))
def testBadConfidenceRaisesException(self):
with self.assertRaises(ValueError):
standard_errors.Jackknife(confidence=95)