def run_one_exp(n,
k,
dim,
ampl,
type_matrix,
scaled,
n_avrg,
type_lap=None,
type_noise='gaussian'):
"""
Run n_avrg experiments for a given set of parameters, and return the mean
kendall-tau score and the associated standard deviation (among the
n_avrg instances).
"""
# Pre-defined settings for Laplacian_Fiedler : if circular, random_walk,
# if linear, unnormalized; Laplacian_init : random_walk
if not type_lap:
type_lap = 'random_walk'
if type_matrix[0] == 'L':
circular = False
elif type_matrix[0] == 'C':
circular = True
else:
raise ValueError("type matrix must be in ['LinearBanded',"
"'CircularBanded', 'LinearStrongDecrease',"
"'CircularStrongDecrease']")
# Create matrix generator
data_gen = MatrixGenerator()
# Create spectral solver
reord_method = SpectralOrdering(dim=dim,
k_nbrs=k,
circular=circular,
scaled=scaled,
type_laplacian=type_lap,
verb=1)
# Initialize array of results
scores = np.zeros(n_avrg)
for i_exp in range(n_avrg):
np.random.seed(i_exp)
data_gen.gen_matrix(n,
type_matrix=type_matrix,
apply_perm=True,
noise_ampl=ampl,
law=type_noise)
this_perm = reord_method.fit_transform(data_gen.sim_matrix)
scores[i_exp] = evaluate_ordering(this_perm,
data_gen.true_perm,
circular=circular)
print('.', end='')
print('')
return (scores.mean(), scores.std(), scores)
type_matrix=type_similarity,
apply_perm=apply_perm,
noise_ampl=ampl_noise,
law=type_noise)
# Call Spectral Ordering method
reord_method = SpectralOrdering(n_components=n_components,
k_nbrs=k_nbrs,
circular=circular,
scale_embedding=scaled,
norm_laplacian='random_walk')
my_perm = reord_method.fit_transform(data_gen.sim_matrix)
reord_method.new_sim = reord_method.new_sim.toarray()
# reord_method.fit(data_gen.sim_matrix)
score = evaluate_ordering(my_perm, data_gen.true_perm, circular=circular)
print("Kendall-Tau score = {}".format(score))
inv_perm = np.argsort(data_gen.true_perm)
# Display some results
fig, axes = plt.subplots(2, 2)
axes[0, 0].tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
axes[0, 0].matshow(data_gen.sim_matrix[:, inv_perm][inv_perm, :])
axes[0, 0].set_title("raw matrix")
axes[0, 1].tick_params(
axis='x', # changes apply to the x-axis
for i_exp in range(10):
np.random.seed(i_exp)
n = 150
data_gen = SimilarityMatrix()
data_gen.gen_matrix(n, type_matrix='CircularBanded', noise_prop=0.1, noise_ampl=5,
apply_perm=False)
X = data_gen.sim_matrix
# plt.matshow(X)
# plt.show()
X = coo_matrix(X)
pp = spectral_eta_trick(X, do_plot=True, n_iter=30, dh=20, circular=True)
pp2 = spectral_eta_trick3(X, do_plot=True, n_iter=30, avg_dim=1)
pp3 = spectral_eta_trick3(X, do_plot=True, dh=20, score_function='Huber', n_iter=100, avg_dim=5, avg_scaling=True)
pp3 = spectral_eta_trick3(ord_mat, do_plot=True, dh=30, score_function='R2S', n_iter=100, avg_dim=8, avg_scaling=True, eigen_solver='amg')
pp = spectral_eta_trick(X, do_plot=True, n_iter=50, dh=20, circular=True, eigen_solver='amg')
kt1 = evaluate_ordering(pp, np.arange(n))
kt2 = evaluate_ordering(pp2, np.arange(n))
print("kt1: %2.2f" % (kt1))
print("kt2: %2.2f" % (kt2))
def run_one_exp(n,
k,
dim,
ampl,
type_matrix,
n_avrg,
type_noise='gaussian',
norm_laplacian='unnormalized',
norm_adjacency=False,
scale_embedding='heuristic',
embedding_method='spectral'):
"""
Run n_avrg experiments for a given set of parameters, and return the mean
kendall-tau score and the associated standard deviation (among the
n_avrg instances).
"""
if type_matrix[0] == 'L':
circular = False
elif type_matrix[0] == 'C':
circular = True
else:
raise ValueError("type matrix must be in ['LinearBanded',"
"'CircularBanded', 'LinearStrongDecrease',"
"'CircularStrongDecrease']")
# Create matrix generator
data_gen = SimilarityMatrix()
# Create spectral solver
if embedding_method == 'TSNE':
reord_method = SpectralOrdering(n_components=2,
k_nbrs=k,
norm_adjacency=norm_adjacency,
norm_laplacian=norm_laplacian,
scale_embedding=scale_embedding,
circular=circular,
merge_if_ccs=True,
embedding_method=embedding_method)
else:
reord_method = SpectralOrdering(n_components=dim,
k_nbrs=k,
norm_adjacency=norm_adjacency,
norm_laplacian=norm_laplacian,
scale_embedding=scale_embedding,
circular=circular,
merge_if_ccs=True,
embedding_method=embedding_method)
# Initialize array of results
scores = np.zeros(n_avrg)
for i_exp in range(n_avrg):
np.random.seed(i_exp)
data_gen.gen_matrix(n,
type_matrix=type_matrix,
apply_perm=True,
noise_ampl=ampl,
law=type_noise)
this_perm = reord_method.fit_transform(data_gen.sim_matrix)
scores[i_exp] = evaluate_ordering(this_perm,
data_gen.true_perm,
circular=circular)
return (scores.mean(), scores.std(), scores)