def test_fit_transform(self):
"""fit transform should give the same result as calling fit and
transform separately"""
arr = np.array([n + 1 for n in range(10)])
np.random.shuffle(arr)
d1 = Discretiser(method="uniform", num_buckets=4)
d1.fit(arr)
r1 = d1.transform(arr)
d2 = Discretiser(method="uniform", num_buckets=4)
r2 = d2.fit_transform(arr)
assert np.array_equal(r1, r2)
def test_fit_transform(self):
"""fit transform should give the same result as calling fit and
transform separately"""
arr = np.array([n + 1 for n in range(10)])
np.random.shuffle(arr)
d1 = Discretiser(method="outlier", outlier_percentile=0.2)
d1.fit(arr)
r1 = d1.transform(arr)
d2 = Discretiser(method="outlier", outlier_percentile=0.2)
r2 = d2.fit_transform(arr)
assert np.array_equal(r1, r2)
def test_fit_transform(self):
"""fit transform should give the same result as calling fit and
transform separately"""
arr = np.array([n + 1 for n in range(10)])
np.random.shuffle(arr)
d1 = Discretiser(method="percentiles",
percentile_split_points=[0.10, 0.40, 0.85])
d1.fit(arr)
r1 = d1.transform(arr)
d2 = Discretiser(method="percentiles",
percentile_split_points=[0.10, 0.40, 0.85])
r2 = d2.fit_transform(arr)
assert np.array_equal(r1, r2)
def test_fit_transform_raises_error(self):
"""since numeric split points are provided, fit will not do anything"""
d = Discretiser(method="fixed", numeric_split_points=[1])
with pytest.raises(RuntimeError):
d.fit_transform(np.array([1]))
