def calc_kl(*, common_info, embedded_neighbors, parameters):
dTSNE_mnist = common_info["dTSNE_mnist"]
X_mnist = common_info["X_mnist"]
Y_mnist = common_info["Y_mnist"]
letter_samples = common_info["letter_samples"]
per_sample_kl_divergences = list()
for j in range(len(letter_samples)):
distance_matrix_dir = distance_matrix_dir_prefix + generate_data.combine_prefixes(
settings.tsne_parameter_set | settings.letter_parameter_set,
parameters, os.sep)
distance_matrix_file = distance_matrix_dir + 'item' + str(j) + '.p'
# Don't store those matrices in a single file. Way too large.
# Make sure you can load them one-by-one.
if os.path.isfile(distance_matrix_file):
if j % 50 == 0:
logging.info("\t%d P-matrix file found. Loading.", j)
with open(distance_matrix_file, 'rb') as f:
new_P, _ = pickle.load(f)
else:
if j % 50 == 0:
logging.info(
"\t%d P-matrix file not found. Creating and saving.", j)
new_X = np.concatenate((X_mnist, letter_samples[j, :].reshape(
(1, -1))),
axis=0)
new_D = distance.squareform(distance.pdist(new_X))
new_P, new_sigmas = lion_tsne.get_p_and_sigma \
(distance_matrix=new_D, perplexity=dTSNE_mnist.perplexity)
if not os.path.isdir(distance_matrix_dir):
logging.info('Creating directory: %s', distance_matrix_dir)
os.mkdir(distance_matrix_dir)
with open(distance_matrix_file, 'wb') as f:
pickle.dump((new_P, new_sigmas), f)
# Single file with p matrix.
# Now use it to calculate KL divergence.
new_Y = np.concatenate((Y_mnist, embedded_neighbors[j, :].reshape(
(1, -1))),
axis=0)
kl, _ = lion_tsne.kl_divergence_and_gradient(p_matrix=new_P, y=new_Y)
per_sample_kl_divergences.append(kl)
return np.mean(per_sample_kl_divergences)
def main(parameters=settings.parameters):
dTSNE_mnist = generate_data.load_dtsne_mnist(parameters=parameters)
picked_neighbors = generate_data.load_picked_neighbors(
parameters=parameters)
Y_mnist = generate_data.load_y_mnist(parameters=parameters)
X_mnist = generate_data.load_x_mnist(parameters=parameters)
accuracy_nn = parameters.get("accuracy_nn",
settings.parameters["accuracy_nn"])
precision_nn = parameters.get("precision_nn",
settings.parameters["precision_nn"])
picked_neighbor_labels = generate_data.load_picked_neighbors_labels(
parameters=parameters)
labels_mnist = generate_data.load_labels_mnist(parameters=parameters)
# =================== ACCURACY
def get_nearest_neighbors_in_y(y, Y_mnist, n=10):
y_distances = np.sum((Y_mnist - y)**2, axis=1)
return np.argsort(y_distances)[:n]
gd_method_list = [
r'Closest $Y_{init}$', r'Random $Y_{init}$',
r'Closest $Y_{init}$; new $\sigma$',
r'Random $Y_{init}$; new $\sigma$', r'Closest $Y_{init}$; EE',
r'Random $Y_{init}$; EE', r'Closest $Y_{init}$; new $\sigma$; EE',
r'Random $Y_{init}$; new $\sigma$; EE'
]
gd_results_file = exp_cluster_attr_test_GD.generate_cluster_results_filename(
parameters=parameters)
with open(gd_results_file, 'rb') as f:
(picked_neighbors_y_gd_transformed,
picked_neighbors_y_gd_variance_recalc_transformed,
picked_neighbors_y_gd_transformed_random,
picked_neighbors_y_gd_variance_recalc_transformed_random,
picked_neighbors_y_gd_early_exagg_transformed_random,
picked_neighbors_y_gd_early_exagg_transformed,
picked_neighbors_y_gd_variance_recalc_early_exagg_transformed_random,
picked_random_starting_positions,
picked_neighbors_y_gd_variance_recalc_early_exagg_transformed,
covered_samples) = pickle.load(f)
gd_method_results = [
picked_neighbors_y_gd_transformed,
picked_neighbors_y_gd_transformed_random,
picked_neighbors_y_gd_variance_recalc_transformed,
picked_neighbors_y_gd_variance_recalc_transformed_random,
picked_neighbors_y_gd_early_exagg_transformed,
picked_neighbors_y_gd_early_exagg_transformed_random,
picked_neighbors_y_gd_variance_recalc_early_exagg_transformed,
picked_neighbors_y_gd_variance_recalc_early_exagg_transformed_random,
]
input_time_file = exp_cluster_attr_test_GD.generate_time_results_filename(
parameters)
with open(input_time_file, 'rb') as f:
picked_neighbors_y_time_gd_transformed, picked_neighbors_y_time_gd_variance_recalc_transformed, \
picked_neighbors_y_time_gd_transformed_random, \
picked_neighbors_y_time_gd_variance_recalc_transformed_random, \
picked_neighbors_y_time_gd_early_exagg_transformed_random, \
picked_neighbors_y_time_gd_early_exagg_transformed, \
picked_neighbors_y_time_gd_variance_recalc_early_exagg_transformed_random, \
picked_neighbors_y_time_gd_variance_recalc_early_exagg_transformed, covered_samples = pickle.load(f)
gd_time = [
np.mean(picked_neighbors_y_time_gd_transformed),
np.mean(picked_neighbors_y_time_gd_transformed_random),
np.mean(picked_neighbors_y_time_gd_variance_recalc_transformed),
np.mean(picked_neighbors_y_time_gd_variance_recalc_transformed_random),
np.mean(picked_neighbors_y_time_gd_early_exagg_transformed),
np.mean(picked_neighbors_y_time_gd_early_exagg_transformed_random),
np.mean(
picked_neighbors_y_time_gd_variance_recalc_early_exagg_transformed
),
np.mean(
picked_neighbors_y_time_gd_variance_recalc_early_exagg_transformed_random
),
]
gd_accuracy = np.zeros((len(gd_method_list, )))
gd_precision = np.zeros((len(gd_method_list, )))
# ============================== Distance percentiles
D_Y = distance.squareform(distance.pdist(Y_mnist))
# Now find distance to closest neighbor
np.fill_diagonal(D_Y, np.inf) # ... but not to itself
nearest_neighbors_y_dist = np.min(D_Y, axis=1) # Actually, whatever axis
gd_nearest_neighbors_percentiles_matrix = np.zeros(
(len(picked_neighbors), len(gd_method_list)))
for i in range(len(picked_neighbors)):
for j in range(len(gd_method_list)):
y = gd_method_results[j][i, :]
nn_dist = np.min(np.sqrt(np.sum((Y_mnist - y)**2, axis=1)))
gd_nearest_neighbors_percentiles_matrix[
i, j] = stats.percentileofscore(nearest_neighbors_y_dist,
nn_dist)
gd_distance_percentiles = np.mean(gd_nearest_neighbors_percentiles_matrix,
axis=0)
for j in range(len(gd_method_list)):
logging.info("%s :\t%f", gd_method_list[j], gd_distance_percentiles[j])
# ============================== KL divergence
for j in range(len(gd_method_results)):
per_sample_accuracy = np.zeros((len(picked_neighbors), ))
per_sample_precision = np.zeros((len(picked_neighbors), ))
for i in range(len(picked_neighbors)):
expected_label = picked_neighbor_labels[i]
nn_indices = get_nearest_neighbors_in_y(gd_method_results[j][i, :],
Y_mnist,
n=accuracy_nn)
obtained_labels = labels_mnist[nn_indices]
per_sample_accuracy[i] = sum(
obtained_labels == expected_label) / len(obtained_labels)
x = picked_neighbors[i, :]
y = gd_method_results[j][i, :]
nn_x_indices = get_nearest_neighbors_in_y(x,
X_mnist,
n=precision_nn)
nn_y_indices = get_nearest_neighbors_in_y(y,
Y_mnist,
n=precision_nn)
matching_indices = len(
[i for i in nn_x_indices if i in nn_y_indices])
per_sample_precision[i] = (matching_indices / precision_nn)
gd_accuracy[j] = np.mean(per_sample_accuracy)
gd_precision[j] = np.mean(per_sample_precision)
logging.info("%s :\t%f\t%f", gd_method_list[j], gd_precision[j],
gd_accuracy[j])
gd_kl = np.zeros((len(gd_method_list), len(picked_neighbors)))
processed_indices = list()
kl_gd_performance_file = generate_gd_kl_temp_filename(parameters)
if os.path.isfile(kl_gd_performance_file):
with open(kl_gd_performance_file, 'rb') as f:
gd_kl, processed_indices = pickle.load(f)
# KL divergence increase for all 1000 samples is very slow to calculate. Main part of that is calculating P-matrix.
per_sample_KL = np.zeros((len(picked_neighbors), ))
for i in range(len(picked_neighbors)):
if i in processed_indices:
logging.info("Sample %d already processed. Results loaded.", i)
continue
logging.info("Processing sample %d", i)
distance_matrix_dir = distance_matrix_dir_prefix + generate_data.combine_prefixes(
settings.tsne_parameter_set
| settings.x_neighbors_selection_parameter_set, parameters, os.sep)
distance_matrix_file = distance_matrix_dir + 'item' + str(j) + '.p'
# Make sure you can load them one-by-one.
if os.path.isfile(distance_matrix_file):
logging.info("\tP-matrix file found. Loading.")
with open(distance_matrix_file, 'rb') as f:
new_P, _ = pickle.load(f)
else:
logging.info("\tP-matrix file not found. Creating and saving.")
new_X = np.concatenate((X_mnist, picked_neighbors[i, :].reshape(
(1, -1))),
axis=0)
new_D = distance.squareform(distance.pdist(new_X))
new_P, new_sigmas = lion_tsne.get_p_and_sigma(
distance_matrix=new_D, perplexity=dTSNE_mnist.perplexity)
with open(distance_matrix_file, 'wb') as f:
pickle.dump((new_P, new_sigmas), f)
# For all of methods P-matrix is shared.
for j in range(len(gd_method_results)):
# Single file with p matrix
new_Y = np.concatenate(
(Y_mnist, gd_method_results[j][i, :].reshape((1, -1))), axis=0)
gd_kl[j,
i], _ = lion_tsne.kl_divergence_and_gradient(p_matrix=new_P,
y=new_Y)
processed_indices.append(i)
with open(kl_gd_performance_file, 'wb') as f:
pickle.dump((gd_kl, processed_indices), f)
# This should be fast
gd_avg_kl = np.mean(gd_kl, axis=1)
output_file = generate_gd_postprocess_filename(parameters)
with open(output_file, "wb") as f:
pickle.dump((gd_method_list, gd_accuracy, gd_precision, gd_time,
gd_avg_kl, gd_distance_percentiles), f)
def main(parameters = settings.parameters):
dTSNE_mnist = generate_data.load_dtsne_mnist(parameters=parameters)
picked_neighbors = generate_data.load_picked_neighbors(parameters=parameters)
Y_mnist = generate_data.load_y_mnist(parameters=parameters)
X_mnist = generate_data.load_x_mnist(parameters=parameters)
accuracy_nn = parameters.get("accuracy_nn", settings.parameters["accuracy_nn"])
precision_nn = parameters.get("precision_nn", settings.parameters["precision_nn"])
picked_neighbor_labels = generate_data.load_picked_neighbors_labels(parameters=parameters)
labels_mnist = generate_data.load_labels_mnist(parameters=parameters)
# =================== Some starting stuff
def get_nearest_neighbors_in_y(y, Y_mnist, n=10):
y_distances = np.sum((Y_mnist - y) ** 2, axis=1)
return np.argsort(y_distances)[:n]
kernelized_results_file = exp_cluster_attr_test_kernelized.generate_cluster_results_filename(parameters)
with open(kernelized_results_file, 'rb') as f:
kernelized_detailed_tsne_method_results, kernelized_detailed_tsne_accuracy, \
kernelized_detailed_tsne_precision, kernelized_detailed_tsne_time, kernelized_detailed_tsne_method_list = pickle.load(f)
ind = [4, 24, 49]
kernelized_method_list = [
kernelized_detailed_tsne_method_list[i][:10] + kernelized_detailed_tsne_method_list[i][-8:]
for i in ind]
kernelized_method_results = [kernelized_detailed_tsne_method_results[i] for i in ind]
kernelized_accuracy = np.zeros((len(kernelized_method_list,)))
kernelized_precision = np.zeros((len(kernelized_method_list,)))
kernelized_per_item_time = np.zeros((len(kernelized_method_list, )))
# ============================== Distance percentiles
D_Y = distance.squareform(distance.pdist(Y_mnist))
# Now find distance to closest neighbor
np.fill_diagonal(D_Y, np.inf) # ... but not to itself
nearest_neighbors_y_dist = np.min(D_Y, axis=1) # Actually, whatever axis
kernelized_nearest_neighbors_percentiles_matrix = np.zeros((len(picked_neighbors), len(kernelized_method_list)))
for i in range(len(picked_neighbors)):
for j in range(len(kernelized_method_list)):
y = kernelized_method_results[j][i, :]
kernelized_dist = np.min(np.sqrt(np.sum((Y_mnist - y) ** 2, axis=1)))
kernelized_nearest_neighbors_percentiles_matrix[i, j] = stats.percentileofscore(nearest_neighbors_y_dist,
kernelized_dist)
kernelized_distance_percentiles = np.mean(kernelized_nearest_neighbors_percentiles_matrix, axis=0)
for j in range(len(kernelized_method_list)):
logging.info("%s %f", kernelized_method_list[j], kernelized_distance_percentiles[j])
# ============================== Accuracy and precision
for j in range(len(kernelized_method_results)):
per_sample_accuracy = np.zeros((len(picked_neighbors),))
per_sample_precision = np.zeros((len(picked_neighbors),))
for i in range(len(picked_neighbors)):
expected_label = picked_neighbor_labels[i]
y = kernelized_method_results[j][i,:]
x = picked_neighbors[i, :]
nn_x_indices = get_nearest_neighbors_in_y(x, X_mnist, n=precision_nn)
nn_y_indices = get_nearest_neighbors_in_y(y, Y_mnist, n=precision_nn)
matching_indices = len([k for k in nn_x_indices if k in nn_y_indices])
per_sample_precision[i] = (matching_indices / precision_nn)
kernelized_indices = get_nearest_neighbors_in_y(kernelized_method_results[j][i,:], Y_mnist, n=accuracy_nn)
obtained_labels = labels_mnist[kernelized_indices]
per_sample_accuracy[i] = sum(obtained_labels==expected_label) / len(obtained_labels)
kernelized_accuracy[j] = np.mean(per_sample_accuracy)
kernelized_precision[j] = np.mean(per_sample_precision)
kernelized_per_item_time[j] = kernelized_detailed_tsne_time[j] / len(picked_neighbors)
logging.info("%s :\t%f\t%f", kernelized_method_list[j], kernelized_precision[j],
kernelized_accuracy[j])
kernelized_kl = np.zeros((len(kernelized_method_list), len(picked_neighbors)))
processed_indices = list()
kl_kernelized_performance_file = generate_kernelized_kl_temp_filename(parameters)
if os.path.isfile(kl_kernelized_performance_file):
with open(kl_kernelized_performance_file, 'rb') as f:
kernelized_kl, processed_indices = pickle.load(f)
# ============================== KL divergence
# KL divergence increase for all 1000 samples is very slow to calculate. Main part of that is calculating P-matrix.
per_sample_KL = np.zeros((len(picked_neighbors),))
for i in range(len(picked_neighbors)):
if i in processed_indices:
logging.info("Sample %d already processed. Results loaded.", i)
continue
logging.info("Processing sample %d", i)
distance_matrix_dir = distance_matrix_dir_prefix + generate_data.combine_prefixes(
settings.tsne_parameter_set | settings.x_neighbors_selection_parameter_set, parameters, os.sep)
distance_matrix_file = distance_matrix_dir + 'item' + str(j) + '.p'
# Make sure you can load them one-by-one.
if os.path.isfile(distance_matrix_file):
logging.info("\tP-matrix file found. Loading.")
with open(distance_matrix_file, 'rb') as f:
new_P, _ = pickle.load(f)
else:
logging.info("\tP-matrix file not found. Creating and saving.")
new_X = np.concatenate((X_mnist, picked_neighbors[i, :].reshape((1, -1))), axis=0)
new_D = distance.squareform(distance.pdist(new_X))
new_P, new_sigmas = lion_tsne.get_p_and_sigma(distance_matrix=new_D, perplexity=dTSNE_mnist.perplexity)
with open(distance_matrix_file, 'wb') as f:
pickle.dump((new_P, new_sigmas), f)
# For all of methods P-matrix is shared.
for j in range(len(kernelized_method_results)):
# Single file with p matrix
new_Y = np.concatenate((Y_mnist, kernelized_method_results[j][i, :].reshape((1, -1))), axis=0)
kernelized_kl[j, i], _ = lion_tsne.kl_divergence_and_gradient(p_matrix=new_P, y=new_Y)
processed_indices.append(i)
with open(kl_kernelized_performance_file, 'wb') as f:
pickle.dump((kernelized_kl, processed_indices), f)
# This should be fast
kernelized_avg_kl = np.mean(kernelized_kl, axis=1)
output_file = generate_kernelized_postprocess_filename(parameters)
with open(output_file, "wb") as f:
pickle.dump((kernelized_method_list, kernelized_accuracy, kernelized_precision,
kernelized_avg_kl, kernelized_per_item_time, kernelized_distance_percentiles),f)
if time_s > 100:
return ">$10^5$"
if time_s > 10:
return ">$10^4$"
if time_s > 1:
return ">$10^3$"
return "%2.2f" % (time_s * 1000)
# ================= GENERATING THE TEX TABLE ==========================
print("\n\nTABLE FOR COPY-PASTING TO LATEX\n\n\n")
s = ""
initial_kl_divergence, _ = lion_tsne.kl_divergence_and_gradient(
y=dTSNE_mnist.Y, p_matrix=dTSNE_mnist.P_matrix)
s += '''\\begin{table} \small\sf\centering \caption{Letters placement test: methods comparison.
Original KL divergence of the dataset is %.5f} \label{tab_letter_methods_comparison}
\\begin{tabular}{ m{0.19\\textwidth} m{0.07\\textwidth} m{0.07\\textwidth} m{0.06\\textwidth} }
\\toprule
\\textbf{Method}
& \\textbf{Distance Perc-le}
& \\textbf{KL Div.}
& \\textbf{Time (ms)}
\\\\ \\midrule''' % (initial_kl_divergence)
s += '\\multicolumn{4}{c}{\\textbf{RBF Interpolation}}\n'
s += '\t\\\\\n'
for j in range(len(rbf_method_list)):
def main(parameters=settings.parameters):
dTSNE_mnist = generate_data.load_dtsne_mnist(parameters=parameters)
Y_mnist = generate_data.load_y_mnist(parameters=parameters)
letter_samples, _, _ = generate_data.load_letters(parameters=parameters)
X_mnist = generate_data.load_x_mnist(parameters=parameters)
D_Y = distance.squareform(distance.pdist(Y_mnist))
# Now find distance to closest neighbor
np.fill_diagonal(D_Y, np.inf) # ... but not to itself
nearest_neighbors_y_dist = np.min(D_Y, axis=1) # Actually, whatever axis
# ============== KL Divergence
gd_method_list = [
r'Closest $Y_{init}$', r'Random $Y_{init}$',
r'Closest $Y_{init}$; new $\sigma$',
r'Random $Y_{init}$; new $\sigma$', r'Closest $Y_{init}$; EE',
r'Random $Y_{init}$; EE', r'Closest $Y_{init}$; new $\sigma$; EE',
r'Random $Y_{init}$; new $\sigma$; EE'
]
gd_results_file = exp_letter_test_GD.generate_letter_results_filename(
parameters=parameters)
with open(gd_results_file, 'rb') as f:
(letters_y_gd_transformed, letters_y_gd_variance_recalc_transformed,
letters_y_gd_transformed_random,
letters_y_gd_variance_recalc_transformed_random,
letters_y_gd_early_exagg_transformed_random,
letters_y_gd_early_exagg_transformed,
letters_y_gd_variance_recalc_early_exagg_transformed_random,
picked_random_starting_positions,
letters_y_gd_variance_recalc_early_exagg_transformed,
covered_samples) = pickle.load(f)
gd_letters_results = [
letters_y_gd_transformed,
letters_y_gd_transformed_random,
letters_y_gd_variance_recalc_transformed,
letters_y_gd_variance_recalc_transformed_random,
letters_y_gd_early_exagg_transformed,
letters_y_gd_early_exagg_transformed_random,
letters_y_gd_variance_recalc_early_exagg_transformed,
letters_y_gd_variance_recalc_early_exagg_transformed_random,
]
input_time_file = exp_letter_test_GD.generate_time_results_filename(
parameters)
with open(input_time_file, 'rb') as f:
letters_y_time_gd_transformed, letters_y_time_gd_variance_recalc_transformed, \
letters_y_time_gd_transformed_random, \
letters_y_time_gd_variance_recalc_transformed_random, \
letters_y_time_gd_early_exagg_transformed_random, \
letters_y_time_gd_early_exagg_transformed, \
letters_y_time_gd_variance_recalc_early_exagg_transformed_random, \
letters_y_time_gd_variance_recalc_early_exagg_transformed, covered_samples = pickle.load(f)
gd_time = [
np.mean(letters_y_time_gd_transformed),
np.mean(letters_y_time_gd_transformed_random),
np.mean(letters_y_time_gd_variance_recalc_transformed),
np.mean(letters_y_time_gd_variance_recalc_transformed_random),
np.mean(letters_y_time_gd_early_exagg_transformed),
np.mean(letters_y_time_gd_early_exagg_transformed_random),
np.mean(letters_y_time_gd_variance_recalc_early_exagg_transformed),
np.mean(
letters_y_time_gd_variance_recalc_early_exagg_transformed_random),
]
gd_letters_kl = np.zeros((len(gd_method_list), len(letter_samples)))
processed_indices = list()
kl_gd_letters_performance_file = generate_gd_kl_temp_filename(parameters)
if os.path.isfile(kl_gd_letters_performance_file):
with open(kl_gd_letters_performance_file, 'rb') as f:
gd_letters_kl, processed_indices = pickle.load(f)
# KL divergence increase for all 1000 samples is very slow to calculate. Main part of that is calculating P-matrix.
per_sample_KL = np.zeros((len(letter_samples), ))
for i in range(len(letter_samples)):
if i in processed_indices:
logging.info("Sample %d already processed. Results loaded.", i)
continue
logging.info("Processing sample %d", i)
distance_matrix_dir = distance_matrix_dir_prefix + generate_data.combine_prefixes(
settings.tsne_parameter_set | settings.letter_parameter_set,
parameters, os.sep)
distance_matrix_file = distance_matrix_dir + 'item' + str(i) + '.p'
# Make sure you can load them one-by-one.
if os.path.isfile(distance_matrix_file):
logging.info("\tP-matrix file found. Loading.")
with open(distance_matrix_file, 'rb') as f:
new_P, _ = pickle.load(f)
else:
logging.info("\tP-matrix file not found. Creating and saving.")
new_X = np.concatenate((X_mnist, letter_samples[i, :].reshape(
(1, -1))),
axis=0)
new_D = distance.squareform(distance.pdist(new_X))
new_P, new_sigmas = lion_tsne.get_p_and_sigma(
distance_matrix=new_D, perplexity=dTSNE_mnist.perplexity)
with open(distance_matrix_file, 'wb') as f:
pickle.dump((new_P, new_sigmas), f)
# For all of methods P-matrix is shared.
for j in range(len(gd_letters_results)):
# Single file with p matrix
new_Y = np.concatenate(
(Y_mnist, gd_letters_results[j][i, :].reshape((1, -1))),
axis=0)
gd_letters_kl[j, i], _ = lion_tsne.kl_divergence_and_gradient(
p_matrix=new_P, y=new_Y)
processed_indices.append(i)
with open(kl_gd_letters_performance_file, 'wb') as f:
pickle.dump((gd_letters_kl, processed_indices), f)
# This should be fast
gd_avg_letters_kl = np.mean(gd_letters_kl, axis=1)
# ============== Distance percentiles
gd_letters_percentiles_matrix = np.zeros(
(len(letter_samples), len(gd_method_list)))
gd_letters_distance_matrix = np.zeros(
(len(letter_samples), len(gd_method_list)))
for i in range(len(letter_samples)):
for j in range(len(gd_method_list)):
y = gd_letters_results[j][i, :]
nn_dist = np.min(np.sqrt(np.sum((Y_mnist - y)**2, axis=1)))
gd_letters_distance_matrix[i, j] = nn_dist
gd_letters_percentiles_matrix[i, j] = stats.percentileofscore(
nearest_neighbors_y_dist, nn_dist)
gd_letters_distance_percentiles = np.mean(gd_letters_percentiles_matrix,
axis=0)
gd_letters_distances = np.mean(gd_letters_distance_matrix, axis=0)
for j in range(len(gd_method_list)):
logging.info("%s: %f, %f", gd_method_list[j], gd_letters_distances[j],
gd_letters_distance_percentiles[j])
output_file = generate_gd_postprocess_filename(parameters)
with open(output_file, "wb") as f:
pickle.dump((gd_method_list, gd_time, gd_avg_letters_kl,
gd_letters_distance_percentiles), f)
def main(parameters=settings.parameters):
dTSNE_mnist = generate_data.load_dtsne_mnist(parameters=parameters)
Y_mnist = generate_data.load_y_mnist(parameters=parameters)
X_mnist = generate_data.load_y_mnist(parameters=parameters)
outlier_samples, _ = generate_data.load_outliers(parameters=parameters)
nn_results_file = exp_outlier_test_NN.generate_outlier_results_filename(
parameters)
with open(nn_results_file, 'rb') as f:
nn_outliers_results, nn_models_orig, nn_method_list = pickle.load(f)
D_Y = distance.squareform(distance.pdist(Y_mnist))
# Now find distance to closest neighbor
np.fill_diagonal(D_Y, np.inf) # ... but not to itself
nearest_neighbors_y_dist = np.min(D_Y, axis=1) # Actually, whatever axis
# ================ KL DIVERGENCE ===================
nn_outliers_kl = np.zeros((len(nn_method_list), len(outlier_samples)))
processed_indices = list()
kl_nn_outliers_performance_file = generate_nn_kl_temp_filename(parameters)
# KL divergence increase for all 1000 samples is very slow to calculate. Main part of that is calculating P-matrix.
per_sample_KL = np.zeros((len(outlier_samples), ))
for i in range(len(outlier_samples)):
if i in processed_indices:
logging.info("Sample %d already processed. Results loaded.", i)
continue
logging.info("Processing sample %d", i)
distance_matrix_dir = distance_matrix_dir_prefix + generate_data.combine_prefixes(
settings.tsne_parameter_set | settings.outlier_parameter_set,
parameters, os.sep)
distance_matrix_file = distance_matrix_dir + 'item' + str(i) + '.p'
# Make sure you can load them one-by-one.
if os.path.isfile(distance_matrix_file):
logging.info("\tP-matrix file found. Loading.")
with open(distance_matrix_file, 'rb') as f:
new_P, _ = pickle.load(f)
else:
logging.info("\tP-matrix file not found. Creating and saving.")
new_X = np.concatenate((X_mnist, outlier_samples[i, :].reshape(
(1, -1))),
axis=0)
new_D = distance.squareform(distance.pdist(new_X))
new_P, new_sigmas = lion_tsne.get_p_and_sigma(
distance_matrix=new_D, perplexity=dTSNE_mnist.perplexity)
with open(distance_matrix_file, 'wb') as f:
pickle.dump((new_P, new_sigmas), f)
# For all of methods P-matrix is shared.
for j in range(len(nn_outliers_results)):
# Single file with p matrix
new_Y = np.concatenate(
(nn_models_orig[j], nn_outliers_results[j][i, :].reshape(
(1, -1))),
axis=0)
nn_outliers_kl[j, i], _ = lion_tsne.kl_divergence_and_gradient(
p_matrix=new_P, y=new_Y)
processed_indices.append(i)
with open(kl_nn_outliers_performance_file, 'wb') as f:
pickle.dump((nn_outliers_kl, processed_indices), f)
# This should be fast
nn_avg_outliers_kl = np.mean(nn_outliers_kl, axis=1)
# ================ DISTANCE MATRICES ===================
nn_outliers_percentiles_matrix = np.zeros(
(len(outlier_samples), len(nn_method_list)))
nn_outliers_distance_matrix = np.zeros(
(len(outlier_samples), len(nn_method_list)))
for i in range(len(outlier_samples)):
for j in range(len(nn_method_list)):
y = nn_outliers_results[j][i, :]
nn_dist = np.min(
np.sqrt(np.sum((nn_models_orig[j] - y)**2, axis=1)))
nn_outliers_distance_matrix[i, j] = nn_dist
nn_outliers_percentiles_matrix[i, j] = stats.percentileofscore(
nearest_neighbors_y_dist, nn_dist)
nn_outliers_distance_percentiles = np.mean(nn_outliers_percentiles_matrix,
axis=0)
nn_outliers_distances = np.mean(nn_outliers_distance_matrix, axis=0)
for j in range(len(nn_method_list)):
print(nn_method_list[j], nn_outliers_distances[j],
nn_outliers_distance_percentiles[j])
output_file = generate_nn_postprocess_filename(parameters)
with open(output_file, "wb") as f:
pickle.dump((nn_method_list, nn_avg_outliers_kl,
nn_outliers_distance_percentiles), f)
def main(parameters=settings.parameters, regenerate=False):
dTSNE_mnist = generate_data.load_dtsne_mnist(parameters=parameters)
Y_mnist = generate_data.load_y_mnist(parameters=parameters)
letter_A_samples, _ = generate_data.load_A_letters(parameters=parameters)
X_mnist = generate_data.load_x_mnist(parameters=parameters)
D_Y = distance.squareform(distance.pdist(Y_mnist))
# Now find distance to closest neighbor
np.fill_diagonal(D_Y, np.inf) # ... but not to itself
nearest_neighbors_y_dist = np.min(D_Y, axis=1) # Actually, whatever axis
kernelized_results_file = exp_letter_A_test_kernelized.generate_letter_A_results_filename(
parameters)
with open(kernelized_results_file, 'rb') as f:
kernelized_detailed_method_results, kernelized_detailed_tsne_time, kernelized_detailed_method_list = pickle.load(
f)
ind = [4, 24, 49]
kernelized_method_list = [
kernelized_detailed_method_list[i][:10] +
kernelized_detailed_method_list[i][-8:] for i in ind
]
kernelized_letters_results = [
kernelized_detailed_method_results[i] for i in ind
]
# =========== DISTANCE PERCENTILES ==========
kernelized_letters_percentiles_matrix = np.zeros(
(len(letter_A_samples), len(kernelized_method_list)))
kernelized_letters_distance_matrix = np.zeros(
(len(letter_A_samples), len(kernelized_method_list)))
for i in range(len(letter_A_samples)):
for j in range(len(kernelized_method_list)):
y = kernelized_letters_results[j][i, :]
nn_dist = np.min(np.sqrt(np.sum((Y_mnist - y)**2, axis=1)))
kernelized_letters_distance_matrix[i, j] = nn_dist
kernelized_letters_percentiles_matrix[i,
j] = stats.percentileofscore(
nearest_neighbors_y_dist,
nn_dist)
kernelized_letters_distance_percentiles = np.mean(
kernelized_letters_percentiles_matrix, axis=0)
kernelized_letters_distances = np.mean(kernelized_letters_distance_matrix,
axis=0)
kernelized_per_item_time = kernelized_detailed_tsne_time / len(
letter_A_samples)
for j in range(len(kernelized_method_list)):
logging.info("%s: %f, %f", kernelized_method_list[j],
kernelized_letters_distances[j],
kernelized_letters_distance_percentiles[j])
kernelized_letters_kl = np.zeros(
(len(kernelized_method_list), len(letter_A_samples)))
processed_indices = list()
kl_kernelized_tsne_letters_performance_file = generate_kernelized_kl_temp_filename(
parameters)
if os.path.isfile(
kl_kernelized_tsne_letters_performance_file) and not regenerate:
with open(kl_kernelized_tsne_letters_performance_file, 'rb') as f:
kernelized_letters_kl, processed_indices = pickle.load(f)
# =========== KL DIVERGENCE ==========
# KL divergence increase for all 1000 samples is very slow to calculate. Main part of that is calculating P-matrix.
per_sample_KL = np.zeros((len(letter_A_samples), ))
for i in range(len(letter_A_samples)):
if i in processed_indices:
logging.info("Sample %d already processed. Results loaded.", i)
continue
logging.info("Processing sample %d", i)
distance_matrix_dir = distance_matrix_dir_prefix + generate_data.combine_prefixes(
settings.tsne_parameter_set | settings.letter_A_parameter_set,
parameters, os.sep)
distance_matrix_file = distance_matrix_dir + 'item' + str(j) + '.p'
# Make sure you can load them one-by-one.
if os.path.isfile(distance_matrix_file):
logging.info("\tP-matrix file found. Loading.")
with open(distance_matrix_file, 'rb') as f:
new_P, _ = pickle.load(f)
else:
logging.info("\tP-matrix file not found. Creating and saving.")
new_X = np.concatenate((X_mnist, letter_A_samples[i, :].reshape(
(1, -1))),
axis=0)
new_D = distance.squareform(distance.pdist(new_X))
new_P, new_sigmas = lion_tsne.get_p_and_sigma(
distance_matrix=new_D, perplexity=dTSNE_mnist.perplexity)
with open(distance_matrix_file, 'wb') as f:
pickle.dump((new_P, new_sigmas), f)
# For all of methods P-matrix is shared.
for j in range(len(kernelized_letters_results)):
# Single file with p matrix
new_Y = np.concatenate(
(Y_mnist, kernelized_letters_results[j][i, :].reshape(
(1, -1))),
axis=0)
kernelized_letters_kl[j,
i], _ = lion_tsne.kl_divergence_and_gradient(
p_matrix=new_P, y=new_Y)
processed_indices.append(i)
with open(kl_kernelized_tsne_letters_performance_file, 'wb') as f:
pickle.dump((kernelized_letters_kl, processed_indices), f)
# This should be fast
kernelized_avg_letters_kl = np.mean(kernelized_letters_kl, axis=1)
output_file = generate_kernelized_postprocess_filename(parameters)
with open(output_file, "wb") as f:
pickle.dump((kernelized_method_list, kernelized_avg_letters_kl,
kernelized_per_item_time,
kernelized_letters_distance_percentiles), f)
