def test_knn_sanity_slow():
if not have_flann:
raise SkipTest("No flann, so skipping knn tests.")
dim = 3
n = 20
np.random.seed(47)
bags = Features(
[np.random.randn(np.random.randint(30, 100), dim) for _ in xrange(n)])
# just make sure it runs
div_funcs = ('kl', 'js', 'renyi:.9', 'l2', 'tsallis:.8')
Ks = (3, 4)
est = KNNDivergenceEstimator(div_funcs=div_funcs, Ks=Ks)
res = est.fit_transform(bags)
assert res.shape == (len(div_funcs), len(Ks), n, n)
assert np.all(np.isfinite(res))
# test that JS blows up when there's a huge difference in bag sizes
# (so that K is too low)
assert_raises(
ValueError,
partial(est.fit_transform, bags + [np.random.randn(1000, dim)]))
# test fit() and then transform() with JS, with different-sized test bags
est = KNNDivergenceEstimator(div_funcs=('js', ), Ks=(5, ))
est.fit(bags, get_rhos=True)
with LogCapture('skl_groups.divergences.knn', level=logging.WARNING) as l:
res = est.transform([np.random.randn(300, dim)])
assert res.shape == (1, 1, 1, len(bags))
assert len(l.records) == 1
assert l.records[0].message.startswith('Y_rhos had a lower max_K')
# test that passing div func more than once raises
def blah(df):
est = KNNDivergenceEstimator(div_funcs=[df, df])
return est.fit(bags)
assert_raises(ValueError, lambda: blah('kl'))
assert_raises(ValueError, lambda: blah('renyi:.8'))
assert_raises(ValueError, lambda: blah('l2'))
def test_knn_sanity_slow():
if not have_flann:
raise SkipTest("No flann, so skipping knn tests.")
dim = 3
n = 20
np.random.seed(47)
bags = Features([np.random.randn(np.random.randint(30, 100), dim)
for _ in xrange(n)])
# just make sure it runs
div_funcs = ('kl', 'js', 'renyi:.9', 'l2', 'tsallis:.8')
Ks = (3, 4)
est = KNNDivergenceEstimator(div_funcs=div_funcs, Ks=Ks)
res = est.fit_transform(bags)
assert res.shape == (len(div_funcs), len(Ks), n, n)
assert np.all(np.isfinite(res))
# test that JS blows up when there's a huge difference in bag sizes
# (so that K is too low)
assert_raises(
ValueError,
partial(est.fit_transform, bags + [np.random.randn(1000, dim)]))
# test fit() and then transform() with JS, with different-sized test bags
est = KNNDivergenceEstimator(div_funcs=('js',), Ks=(5,))
est.fit(bags, get_rhos=True)
with LogCapture('skl_groups.divergences.knn', level=logging.WARNING) as l:
res = est.transform([np.random.randn(300, dim)])
assert res.shape == (1, 1, 1, len(bags))
assert len(l.records) == 1
assert l.records[0].message.startswith('Y_rhos had a lower max_K')
# test that passing div func more than once raises
def blah(df):
est = KNNDivergenceEstimator(div_funcs=[df, df])
return est.fit(bags)
assert_raises(ValueError, lambda: blah('kl'))
assert_raises(ValueError, lambda: blah('renyi:.8'))
assert_raises(ValueError, lambda: blah('l2'))
def distribution_divergence(X_s, X_l, k=10):
"""
This function computes l2 and js divergences from samples of two distributions.
The implementation use `skl-groups`, which implements non-parametric estimation
of divergences.
Args:
+ X_s: a numpy array containing point cloud in state space
+ X_e: a numpy array containing point cloud in latent space
"""
# We discard cases with too large dimensions
if X_s.shape[1] > 50:
return {'l2_divergence': -1., 'js_divergence': -1.}
# We instantiate the divergence object
div = KNNDivergenceEstimator(div_funcs=['l2', 'js'],
Ks=[k],
n_jobs=4,
clamp=True)
# We turn both data to float32
X_s = X_s.astype(np.float32)
X_l = X_l.astype(np.float32)
# We generate Features
f_s = Features(X_s, n_pts=[X_s.shape[0]])
f_l = Features(X_l, n_pts=[X_l.shape[0]])
# We create the knn graph
div.fit(X=f_s)
# We compute the divergences
l2, js = div.transform(X=f_l).squeeze()
# We construct the returned dictionnary
output = {'l2_divergence': l2, 'js_divergence': js}
return output
def test_knn_js():
if not have_flann:
raise SkipTest("No flann, so skipping knn tests.")
# verified by hand
x = np.reshape([0., 1, 3, 6], (4, 1))
n = 4
y = np.reshape([.2, 1.2, 3.2, 6.2, 10.2], (5, 1))
m = 5
M = 2
right_js = (
np.log(n + m - 1) + psi(M)
+ 1/(2*n) * ( # x weight is 1/7, y weight is 4/35, quantile 1/4
np.log(.2) - psi(1) # 0 => .2(y), 1(x)
+ np.log(.8) - psi(2) # 1 => 1.2(y), .2(y)
+ np.log(1.8) - psi(2) # 3 => 3.2(y), 1.2(y)
+ np.log(2.8) - psi(2) # 6 => 6.2(y), 3.2(y)
)
+ 1/(2*m) * ( # x weight is 5/36, y weight is 1/9, quantile 1/4
np.log(.2) - psi(1) # .2 => 0(x)
+ np.log(1) - psi(2) # 1.2 => 1(x), .2(y)
+ np.log(2) - psi(2) # 3.2 => 3(x), 1.2(y)
+ np.log(3) - psi(2) # 6.2 => 6(x), 3.2(y)
+ np.log(4.2) - psi(2) # 10.2 => 6.2(y), 6(x)
)
- 1/2 * np.log(n-1) - 1/(2*n) * (
np.log(3) + np.log(2) + np.log(3) + np.log(5))
- 1/2 * np.log(m-1) - 1/(2*m) * (
np.log(3) + np.log(2) + np.log(3) + np.log(4) + np.log(7))
)
msg = "got {}, expected {}"
est = KNNDivergenceEstimator(div_funcs=['js'], Ks=[2], clamp=False)
res = est.fit([x]).transform([y])
assert res.shape == (1, 1, 1, 1)
res = res[0, 0, 0, 0]
assert np.allclose(res, right_js, atol=1e-6), msg.format(res, right_js)
def test_knn_js():
if not have_flann:
raise SkipTest("No flann, so skipping knn tests.")
# verified by hand
x = np.reshape([0., 1, 3, 6], (4, 1))
n = 4
y = np.reshape([.2, 1.2, 3.2, 6.2, 10.2], (5, 1))
m = 5
M = 2
right_js = (
np.log(n + m - 1) + psi(M) + 1 / (2 * n) *
( # x weight is 1/7, y weight is 4/35, quantile 1/4
np.log(.2) - psi(1) # 0 => .2(y), 1(x)
+ np.log(.8) - psi(2) # 1 => 1.2(y), .2(y)
+ np.log(1.8) - psi(2) # 3 => 3.2(y), 1.2(y)
+ np.log(2.8) - psi(2) # 6 => 6.2(y), 3.2(y)
) + 1 / (2 * m) * ( # x weight is 5/36, y weight is 1/9, quantile 1/4
np.log(.2) - psi(1) # .2 => 0(x)
+ np.log(1) - psi(2) # 1.2 => 1(x), .2(y)
+ np.log(2) - psi(2) # 3.2 => 3(x), 1.2(y)
+ np.log(3) - psi(2) # 6.2 => 6(x), 3.2(y)
+ np.log(4.2) - psi(2) # 10.2 => 6.2(y), 6(x)
) - 1 / 2 * np.log(n - 1) - 1 / (2 * n) *
(np.log(3) + np.log(2) + np.log(3) + np.log(5)) -
1 / 2 * np.log(m - 1) - 1 / (2 * m) *
(np.log(3) + np.log(2) + np.log(3) + np.log(4) + np.log(7)))
msg = "got {}, expected {}"
est = KNNDivergenceEstimator(div_funcs=['js'], Ks=[2], clamp=False)
res = est.fit([x]).transform([y])
assert res.shape == (1, 1, 1, 1)
res = res[0, 0, 0, 0]
assert np.allclose(res, right_js, atol=1e-6), msg.format(res, right_js)
def blah(df):
est = KNNDivergenceEstimator(div_funcs=[df, df])
return est.fit(bags)
def blah(df):
est = KNNDivergenceEstimator(div_funcs=[df, df])
return est.fit(bags)
