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Fix tests #79

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29e68bc
Fix np.int and np.complex_ deprecations
stephen-huan Mar 7, 2025
c239ff2
Disable sum check in chisquare tests
stephen-huan Mar 7, 2025
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2 changes: 1 addition & 1 deletion dppy/exotic_dpps_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,7 +118,7 @@ def ust_sampler_aldous_broder(list_of_neighbors, root=None,
visited[n0] = True
nb_nodes_in_tree = 1

tree_edges = np.zeros((nb_nodes - 1, 2), dtype=np.int)
tree_edges = np.zeros((nb_nodes - 1, 2), dtype=int)

while nb_nodes_in_tree < nb_nodes:

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12 changes: 6 additions & 6 deletions dppy/random_matrices.py
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Expand Up @@ -334,7 +334,7 @@ def mu_ref_unif_unit_circle_sampler_quindiag(beta=2, size=10,
raise ValueError('`beta` must be positive integer.\
Given: {}'.format(beta))

alpha = np.zeros(size, dtype=np.complex_)
alpha = np.zeros(size, dtype=np.complex128)

# nu = 1 + beta*(N-1, N-2, ..., 0)
for i, nu in enumerate(1 + beta * np.arange(size - 1, -1, step=-1)):
Expand All @@ -343,7 +343,7 @@ def mu_ref_unif_unit_circle_sampler_quindiag(beta=2, size=10,

rho = np.sqrt(1 - np.abs(alpha[:-1])**2)

xi = np.zeros((size - 1, 2, 2), dtype=np.complex_) # xi[0,..,N-1]
xi = np.zeros((size - 1, 2, 2), dtype=np.complex128) # xi[0,..,N-1]
xi[:, 0, 0], xi[:, 0, 1] = alpha[:-1].conj(), rho
xi[:, 1, 0], xi[:, 1, 1] = rho, -alpha[:-1]

Expand All @@ -352,14 +352,14 @@ def mu_ref_unif_unit_circle_sampler_quindiag(beta=2, size=10,
# xi[N-1] = alpha[N-1].conj()
if size % 2 == 0: # even
L = sp.block_diag(xi[::2, :, :],
dtype=np.complex_)
dtype=np.complex128)
M = sp.block_diag([1.0, *xi[1::2, :, :], alpha[-1].conj()],
dtype=np.complex_)
dtype=np.complex128)
else: # odd
L = sp.block_diag([*xi[::2, :, :], alpha[-1].conj()],
dtype=np.complex_)
dtype=np.complex128)
M = sp.block_diag([1.0, *xi[1::2, :, :]],
dtype=np.complex_)
dtype=np.complex128)

return la.eigvals(L.dot(M).toarray())

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2 changes: 1 addition & 1 deletion tests/test_beta_ensembles_samplers.py
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Expand Up @@ -42,7 +42,7 @@ def limiting_distribution_adequation(samples,
f_th = [quad(limiting_distribution, a, b)[0]
for a, b in zip(bins[:-1], bins[1:])]

_, pval = chisquare(f_obs=f_emp, f_exp=f_th)
_, pval = chisquare(f_obs=f_emp, f_exp=f_th, sum_check=False)

msg = 'pval = {}'.format(pval)

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4 changes: 3 additions & 1 deletion tests/test_finite_dpps_samplers.py
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Expand Up @@ -90,7 +90,9 @@ def doubleton_adequation(dpp, samples, tol=0.05):
for doubl in doubletons]
marginal_emp = np.array(counts) / len(samples)

_, pval = chisquare(f_obs=marginal_emp, f_exp=marginal_th)
_, pval = chisquare(f_obs=marginal_emp,
f_exp=marginal_th,
sum_check=False)

msg = 'pval = {}, emp = {}, th = {}'.format(pval,
marginal_emp,
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