def test_equal_entries(self):
settings = {"cubes": 10, "overlap": 0.5}
# uniform data:
data = np.arange(0, 100)
data = data[:, np.newaxis]
lens = data
cov = Cover(settings["cubes"], settings["overlap"])
# Prefix'ing the data with an ID column
ids = np.array([x for x in range(lens.shape[0])])
lens = np.c_[ids, lens]
bins = cov.fit(lens)
bins = list(bins) # extract list from generator
assert len(bins) == settings["cubes"]
cube_entries = [cov.transform_single(lens, cube) for cube in bins]
for c1, c2 in list(zip(cube_entries, cube_entries[1:]))[2:]:
c1, c2 = c1[:, 0], c2[:, 0] # indices only
calced_overlap = len(set(list(c1)).intersection(set(list(c2)))) / max(
len(c1), len(c2)
)
assert calced_overlap == pytest.approx(0.5)
def test_bounds(self):
data_vals = np.arange(40).reshape(20, 2)
data = np.zeros((20, 3))
data[:, 0] = np.arange(20, dtype=int) # Index row
data[:, 1:3] = data_vals
limits = np.array([[np.float("inf"), np.float("inf")], [-10, 100]])
cover = Cover(n_cubes=10, limits=limits)
cubes = cover.fit(data)
assert np.array_equal(cover.bounds_, np.array([[0, -10], [38, 100]]))
def test_radius_dist(self):
test_cases = [
{
"cubes": 1,
"range": [0, 4],
"overlap": 0.4,
"radius": 10.0 / 3
},
{
"cubes": 1,
"range": [0, 4],
"overlap": 0.9,
"radius": 20.0
},
{
"cubes": 2,
"range": [-4, 4],
"overlap": 0.5,
"radius": 4.0
},
{
"cubes": 3,
"range": [-4, 4],
"overlap": 0.5,
"radius": 2.666666666
},
{
"cubes": 10,
"range": [-4, 4],
"overlap": 0.5,
"radius": 0.8
},
{
"cubes": 10,
"range": [-4, 4],
"overlap": 1.0,
"radius": np.inf
},
]
for test_case in test_cases:
scaler = preprocessing.MinMaxScaler(
feature_range=test_case["range"])
data = scaler.fit_transform(np.arange(20).reshape(10, 2))
cover = Cover(n_cubes=test_case["cubes"],
perc_overlap=test_case["overlap"])
_ = cover.fit(data)
assert cover.radius_[0] == pytest.approx(test_case["radius"])
def test_entries_in_correct_cubes(self):
# TODO: this test is a little hacky
data_vals = np.arange(20)
data = np.zeros((20, 2))
data[:, 0] = np.arange(20, dtype=int) # Index row
data[:, 1] = data_vals
cover = Cover(n_cubes=10, perc_overlap=0.2)
cubes = cover.fit(data)
cubes = list(cubes)
entries = [cover.transform_single(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_perc_overlap(self, CoverClass):
"""
2 cubes with 50% overlap and a range of [0,1] should lead to two cubes with intervals:
[0, .75]
[.25, 1]
"""
data = np.array([[0, 0], [1, 0.25], [2, 0.5], [3, 0.75], [4, 1]])
cover = Cover(n_cubes=2, perc_overlap=0.5)
cubes = cover.fit(data)
cubes = list(cubes)
entries = [cover.transform_single(data, cube) for cube in cubes]
for i in (0, 1, 2, 3):
assert data[i] in entries[0]
for i in (1, 2, 3, 4):
assert data[i] in entries[1]
def test_125_replication(self):
# uniform data:
data = np.arange(0, 100)
data = data[:, np.newaxis]
lens = data
cov = Cover(10, 0.5)
# Prefix'ing the data with an ID column
ids = np.array([x for x in range(lens.shape[0])])
lens = np.c_[ids, lens]
bins = cov.fit(lens)
cube_entries = [cov.transform_single(lens, cube) for cube in bins]
overlaps = [
len(set(list(c1[:, 0])).intersection(set(list(c2[:, 0]))))
for c1, c2 in zip(cube_entries, cube_entries[1:])
]
assert (len(set(overlaps)) == 1
), "Each overlap should have the same number of entries. "
def test_define_diff_bins_per_dim(self):
data = np.arange(30).reshape(10, 3)
c = Cover(n_cubes=[5, 10])
cubes = c.fit(data)
assert len(list(cubes)) == 5 * 10
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_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])
import numpy as np
from kmapper.cover import Cover
# uniform data:
data = np.arange(0, 1000).reshape((1000, 1))
lens = data
cov = Cover(10, 0.5, verbose=0)
def overlap(c1, c2):
ints = set(c1).intersection(set(c2))
return len(ints) / max(len(c1), len(c2))
# Prefix'ing the data with an ID column
ids = np.array([x for x in range(lens.shape[0])])
lens = np.c_[ids, lens]
bins = cov.fit(lens)
cube_entries = cov.transform(lens, bins)
for i, hypercube in enumerate(cube_entries):
print("There are %s points in cube %s/%s" %
(hypercube.shape[0], i, len(cube_entries)))
print()
for i, (c1, c2) in enumerate(zip(cube_entries, cube_entries[1:])):
print("Overlap %s" % (overlap(c1[:, 0], c2[:, 0])))