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 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 testComparisonBaslineGivesError(self):
data = pd.DataFrame({"X": [1, 2, 3, 4, 5], "Y": [1, 1, 1, 1, 1]})
metric = metrics.Sum("X")
comparison = comparisons.AbsoluteDifference("Y", 0)
with self.assertRaises(ValueError):
core.Analyze(data).relative_to(comparison).calculate(metric).run()
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 testBadWhereRaisesError(self):
data = pd.DataFrame({
"X": (1, 2, 3, 10, 20, 30, 100, 200, 300),
"Y": (0, 1, 2, 3, 4, 5, 6, 7, 8)
})
metric = metrics.Sum("X")
with self.assertRaises(ValueError):
core.Analyze(data).where("X + Y").calculate(metric).run()
def testNamingCalculations(self):
data = pd.DataFrame({"X": [1, 2, 3, 4, 5]})
metric = metrics.Sum("X", name="X-Total")
output = core.Analyze(data).calculate(metric).run()
correct = pd.DataFrame(np.array([[15]]), columns=["X-Total"])
self.assertTrue(output.equals(correct))
def testSumWithWeights(self):
df = pd.DataFrame({"X": [1, 2, 3, 4]})
weights = np.array([3, 2, 1, 1])
metric = metrics.Sum("X")
output = metric(df, weights)
correct = 14
self.assertEqual(output, correct)
def testMultipleCalculationsRelativeTo(self):
data = pd.DataFrame({
"X": (1, 2, 3, 10, 20, 30, 100, 200, 300),
"Y": (0, 1, 2, 3, 4, 5, 6, 7, 8),
"Experiment": ("Control", "Control", "Control", "Exp1", "Exp1",
"Exp1", "Exp2", "Exp2", "Exp2")
})
comparison = comparisons.AbsoluteDifference("Experiment", "Control")
output = core.Analyze(data).relative_to(comparison).calculate(
(metrics.Sum("X"), metrics.Sum("Y"))).run()
correct = pd.DataFrame(
{
"sum(X) Absolute Difference": (60 - 6, 600 - 6),
"sum(Y) Absolute Difference": (12 - 3, 21 - 3)
},
index=pd.Index(("Exp1", "Exp2"), name="Experiment"))
self.assertTrue(output.equals(correct))
def testMultipleCalculations(self):
data = pd.DataFrame({"X": [1, 2, 3, 4, 5]})
output = core.Analyze(data).calculate(
[metrics.Sum("X"), metrics.Mean("X")]).run()
correct = pd.DataFrame(np.array([[15, 3.0]]),
columns=["sum(X)", "mean(X)"])
correct[["sum(X)"]] = correct[["sum(X)"]].astype(int)
self.assertTrue(output.equals(correct))
def testWhere(self):
data = pd.DataFrame({
"X": (1, 2, 3, 10, 20, 30, 100, 200, 300),
"Y": (0, 1, 2, 3, 4, 5, 6, 7, 8)
})
metric = metrics.Sum("X")
output = core.Analyze(data).where("Y >= 4").calculate(metric).run()
correct = pd.DataFrame(np.array([[650]]), columns=["sum(X)"])
self.assertTrue(output.equals(correct))
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 testPercentageDifference(self):
data = pd.DataFrame({
"X": [1, 3, 2, 3, 1, 4],
"Condition": [0, 0, 0, 1, 1, 1]
})
weights = np.ones(6)
comparison = comparisons.PercentageDifference("Condition", 0)
comparison.precalculate_factors(data)
metric = metrics.Sum("X")
output = comparison(data, weights, metric).values[0]
self.assertEqual(100 * (8 - 6) / 6, output)
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 testSplitBy(self):
data = pd.DataFrame({
"X": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"Y": [1, 1, 2, 2, 3, 3, 4, 4, 5, 5]
})
metric = metrics.Sum("X")
output = core.Analyze(data).calculate(metric).split_by("Y").run()
correct = pd.DataFrame({
"sum(X)": [1, 5, 9, 13, 17],
"Y": [1, 2, 3, 4, 5]
})
correct = correct.set_index("Y")
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 testMultipleSplitBy(self):
data = pd.DataFrame({
"X": [4, 5, 6, 7, 0, 1, 2, 3],
"Y": [1, 1, 1, 1, 0, 0, 0, 0],
"Z": [0, 0, 1, 1, 0, 0, 1, 1]
})
metric = metrics.Sum("X")
output = core.Analyze(data).split_by(["Y",
"Z"]).calculate(metric).run()
correct = pd.DataFrame({
"sum(X)": [1, 5, 9, 13],
"Y": [0, 0, 1, 1],
"Z": [0, 1, 0, 1]
})
correct = correct.set_index(["Y", "Z"])
self.assertTrue(output.equals(correct))
def testSortFalse(self):
data = pd.DataFrame({
"X": [6, 5, 4, 7, 0, 1, 2, 3],
"Y": [1, 1, 1, 1, 0, 0, 0, 0],
"Z": [1, 0, 0, 1, 0, 0, 1, 1]
})
metric = metrics.Sum("X")
output = core.Analyze(data).split_by(
["Y", "Z"]).calculate(metric).run(sort=False)
correct = pd.DataFrame({
"sum(X)": [13, 9, 1, 5],
"Y": [1, 1, 0, 0],
"Z": [1, 0, 0, 1]
})
correct = correct.set_index(["Y", "Z"])
self.assertTrue(output.equals(correct))
def testRelativeTo(self):
data = pd.DataFrame({
"X": [1, 2, 3, 10, 20, 30, 100, 200, 300],
"Y": [0, 0, 0, 1, 1, 1, 2, 2, 2]
})
metric = metrics.Sum("X")
comparison = comparisons.AbsoluteDifference("Y", 0)
output = core.Analyze(data).relative_to(comparison).calculate(
metric).run()
correct = pd.DataFrame({
"sum(X) Absolute Difference": [60 - 6, 600 - 6],
"Y": [1, 2]
})
correct = correct.set_index("Y")
self.assertTrue(output.equals(correct))
def testRelativeToSplitsWithNoAlternativeGivesNaN(self):
data = pd.DataFrame({
"X": [1, 2, 3, 4],
"Y": [0, 0, 0, 1],
"Z": [0, 0, 1, 1]
})
metric = metrics.Sum("X")
comparison = comparisons.AbsoluteDifference("Y", 0)
output = core.Analyze(data).split_by("Z").relative_to(
comparison).calculate(metric).run()
correct = pd.DataFrame({
"sum(X) Absolute Difference": [np.nan, 4 - 3],
"Z": [0, 1],
"Y": [1, 1]
})
correct = correct.set_index(["Z", "Y"])
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 testRelativeToSplit(self):
data = pd.DataFrame({
"X": [1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8],
"Y": [0, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 1, 1, 1, 2, 2, 2],
"Z": [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
})
metric = metrics.Sum("X")
comparison = comparisons.AbsoluteDifference("Y", 0)
output = core.Analyze(data).split_by("Z").relative_to(
comparison).calculate(metric).run()
correct = pd.DataFrame({
"sum(X) Absolute Difference": [13 - 5, 23 - 5, 14 - 4, 22 - 4],
"Z": [0, 0, 1, 1],
"Y": [1, 2, 1, 2]
})
correct = correct.set_index(["Z", "Y"])
self.assertTrue(output.equals(correct))
