def test_cube_dim(self):
data = np.arange(30).reshape(10, 3)
c = Cover(nr_cubes=10)
cubes = c.define_bins(data)
assert all(len(cube) == 2 for cube in cubes)
def test_chunk_dist(self):
data = np.arange(20).reshape(10, 2)
cover = Cover(nr_cubes=10)
_ = cover.define_bins(data)
chunks = list(cover.chunk_dist)
# TODO: this test is really fagile and has magic number, fix.
assert all(i == 1.8 for i in chunks)
def test_entries_even(self):
data = np.arange(40).reshape(20, 2)
cover = Cover(nr_cubes=10)
cubes = cover.define_bins(data)
for cube in cubes:
entries = cover.find_entries(data, cube)
assert len(entries) >= 2
def test_cubes_overlap(self):
data = np.arange(40).reshape(20, 2)
cover = Cover(nr_cubes=10)
cubes = cover.define_bins(data)
entries = []
for cube in cubes:
# turn singleton lists into individual elements
res = [i[0] for i in cover.find_entries(data, cube)]
entries.append(res)
for i, j in zip(range(9), range(1, 10)):
assert set(entries[i]).union(set(entries[j]))
def test_entries_in_correct_cubes(self):
# TODO: this test is a little hacky
data = np.arange(40).reshape(20, 2)
cover = Cover(nr_cubes=10)
cubes = cover.define_bins(data)
cubes = list(cubes)
entries = [cover.find_entries(data, cube) for cube in cubes]
# inside of each cube is there. Sometimes the edges don't line up.
for i in range(10):
assert data[2 * i] in entries[i]
assert data[2 * i + 1] in entries[i]
def test_define_diff_bins_per_dim(self):
data = np.arange(30).reshape(10, 3)
c = Cover(nr_cubes=[5, 10])
cubes = c.define_bins(data)
assert len(list(cubes)) == 5 * 10
def test_bound_is_min(self):
data = np.arange(30).reshape(10, 3)
cov = Cover(nr_cubes=10)
_ = cov.define_bins(data)
bounds = list(zip(cov.d, range(1, 10)))
assert all(b[0] == b[1] for b in bounds)
def test_nr_dimensions(self):
data = np.arange(30).reshape(10, 3)
c = Cover(nr_cubes=10)
_ = c.define_bins(data)
assert c.nr_dimensions == 2
def test_nr_dimensions(self):
data = np.arange(30).reshape(10, 3)
c = Cover(nr_cubes=10)
_ = c.define_bins(data)
assert c.nr_dimensions == 2
def test_single_dim(self):
data = np.arange(20).reshape(10, 2)
c = Cover(nr_cubes=10)
cubes = c.define_bins(data)
assert all(len(cube) == 1 for cube in cubes)
def test_diff_overlap_per_dim(self):
data = np.random.rand(100, 3)
c = Cover(perc_overlap=[0.4, 0.2])
c.fit(data)
def test_cube_count(self):
data = np.arange(30).reshape(10, 3)
c = Cover(nr_cubes=10)
cubes = c.define_bins(data)
assert len(list(cubes)) == 10**2, "idx column is ignored"
def test_single_dim(self):
data = np.arange(20).reshape(10, 2)
c = Cover(nr_cubes=10)
cubes = c.define_bins(data)
assert all(len(cube) == 1 for cube in cubes)
def test_find_entries_runs_with_diff_bins(self):
data = np.arange(30).reshape(10, 3)
c = Cover(n_cubes=[5, 10])
cubes = list(c.define_bins(data))
_ = c.find_entries(data, cubes[0])
def test_define_diff_bins_per_dim(self):
data = np.arange(30).reshape(10, 3)
c = Cover(n_cubes=[5, 10])
cubes = c.define_bins(data)
assert len(list(cubes)) == 5 * 10
def test_diff_overlap_per_dim(self):
data = np.random.rand(100, 10)
c = Cover(overlap_perc=[2, 10])
def test_bound_is_min(self):
data = np.arange(30).reshape(10, 3)
cov = Cover(n_cubes=10)
_ = cov.fit(data)
bounds = list(zip(cov.bounds_[0], range(1, 10)))
assert all(b[0] == b[1] for b in bounds)
def test_transform_runs_with_diff_bins(self):
data = np.arange(30).reshape(10, 3)
c = Cover(n_cubes=[5, 10])
cubes = list(c.fit(data))
_ = c.transform_single(data, cubes[0])
def test_find_entries_runs_with_diff_bins(self):
data = np.arange(30).reshape(10, 3)
c = Cover(nr_cubes=[5, 10])
cubes = list(c.define_bins(data))
_ = c.find_entries(data, cubes[0])
def test_cube_count(self):
data = np.arange(30).reshape(10, 3)
c = Cover(nr_cubes=10)
cubes = c.define_bins(data)
assert len(list(cubes)) == 10**2, "idx column is ignored"
