matrix_scores_val /= transformer.n_transforms
labels = y_test.flatten() == single_class_ind
# plot_matrix_score(x_test, matrix_scores, labels, plot_inliers=True,
# n_to_plot=5)
# plot_matrix_score(x_test, matrix_scores, labels, plot_inliers=False,
# n_to_plot=5)
plot_histogram_disc_loss_acc_thr(
np.trace(matrix_scores_test[labels], axis1=1, axis2=2),
np.trace(matrix_scores_test[~labels], axis1=1, axis2=2),
path='../results',
x_label_name='TransTransformations_matrixTrace_hits',
val_inliers_score=np.trace(matrix_scores_val))
entropy_scores_test = get_entropy(matrix_scores_test)
entropy_scores_val = get_entropy(matrix_scores_val)
plot_histogram_disc_loss_acc_thr(
entropy_scores_test[labels],
entropy_scores_test[~labels],
path='../results',
x_label_name='TransTransformations_entropy_scores_hits',
val_inliers_score=entropy_scores_val)
## Get logits for xentropy
# Get matrix scores
matrix_scores_raw_test = np.zeros(
(len(x_test), transformer.n_transforms, transformer.n_transforms))
for t_ind in tqdm(range(transformer.n_transforms)):
test_specific_transform_indxs = np.where(
transformations_inds_test == t_ind)
def test_model_original(transformer,
loader,
dataset_name='hits-4-c',
single_class_ind=1):
results_dir = os.path.join(PROJECT_PATH, 'tests', 'aux_results')
save_dir = os.path.join(PROJECT_PATH, 'tests', 'aux_data')
utils.check_path(results_dir)
utils.check_path(save_dir)
utils.check_path(os.path.join(results_dir, dataset_name))
# load-save data
(x_train, y_train), (x_val, y_val), (x_test,
y_test) = loader(return_val=True)
normal_data = (x_train, y_train), (x_val, y_val), (x_test, y_test)
utils.save_pickle(
normal_data,
os.path.join(save_dir,
'normal_data_%s_tf1_original.pkl' % dataset_name))
# create model
n, k = (10, 4)
mdl = create_wide_residual_network(x_train.shape[1:],
transformer.n_transforms, n, k)
mdl.compile('adam', 'categorical_crossentropy', ['acc'])
# get inliers of specific class
# get inliers
x_train_task = x_train[y_train.flatten() == single_class_ind]
print(x_train_task.shape)
# transform inliers
transformations_inds = np.tile(np.arange(transformer.n_transforms),
len(x_train_task))
x_train_task_transformed = transformer.transform_batch(
np.repeat(x_train_task, transformer.n_transforms, axis=0),
transformations_inds)
print(x_train_task_transformed.shape)
# train model
batch_size = 128
mdl.fit(x=x_train_task_transformed,
y=to_categorical(transformations_inds),
batch_size=batch_size,
epochs=int(np.ceil(200 / transformer.n_transforms)))
scores = np.zeros((len(x_test), ))
matrix_evals = np.zeros(
(len(x_test), transformer.n_transforms, transformer.n_transforms))
observed_data = x_train_task
for t_ind in range(transformer.n_transforms):
observed_dirichlet = mdl.predict(transformer.transform_batch(
observed_data, [t_ind] * len(observed_data)),
batch_size=1024)
log_p_hat_train = np.log(observed_dirichlet).mean(axis=0)
alpha_sum_approx = calc_approx_alpha_sum(observed_dirichlet)
alpha_0 = observed_dirichlet.mean(axis=0) * alpha_sum_approx
mle_alpha_t = fixed_point_dirichlet_mle(alpha_0, log_p_hat_train)
x_test_p = mdl.predict(transformer.transform_batch(
x_test, [t_ind] * len(x_test)),
batch_size=1024)
matrix_evals[:, :, t_ind] += x_test_p
scores += dirichlet_normality_score(mle_alpha_t, x_test_p)
scores /= transformer.n_transforms
matrix_evals /= transformer.n_transforms
scores_simple = np.trace(matrix_evals, axis1=1, axis2=2)
scores_entropy = -1 * get_entropy(matrix_evals)
scores_xH = -1 * get_xH(transformer, matrix_evals)
labels = y_test.flatten() == single_class_ind
save_results_file(results_dir,
dataset_name,
single_class_ind,
scores=scores,
labels=labels,
experiment_name='transformations')
save_results_file(results_dir,
dataset_name,
single_class_ind,
scores=scores_simple,
labels=labels,
experiment_name='transformations-simple')
save_results_file(results_dir,
dataset_name,
single_class_ind,
scores=scores_entropy,
labels=labels,
experiment_name='transformations-entropy')
save_results_file(results_dir,
dataset_name,
single_class_ind,
scores=scores_xH,
labels=labels,
experiment_name='transformations-xH')
mdl_weights_name = '{}_tf1_original_{}_weights.h5'.format(
dataset_name, get_class_name_from_index(single_class_ind,
dataset_name))
mdl_weights_path = os.path.join(results_dir, dataset_name,
mdl_weights_name)
mdl.save_weights(mdl_weights_path)
"""
Time test_model_original(transformer, load_hits4c, dataset_name='hits-4-c') 00:06:58.37
(0.9917134999999999, 0.9350055, 0.9872614999999999, 0.94142025)
(0.9938067500000001, 0.9923547500000001, 0.9931685, 0.992637375)
(0.9912172499999999, 0.9883357499999998, 0.9909070000000001, 0.9886706249999999)
#train only Time test_model_original(transformer, load_hits4c, dataset_name='hits-4-c', tf_version='tf1') 00:03:48.29
"""
return get_roc_auc(scores, labels), get_roc_auc(scores_simple, labels), \
get_roc_auc(scores_entropy, labels), get_roc_auc(scores_xH, labels)
def test_model_loading(transformer,
mdl,
loader,
dataset_name='hits-4-c',
single_class_ind=1,
tf_version='tf1',
transformer_name='transformed',
model_name='resnet',
epochs=None):
results_dir = os.path.join(PROJECT_PATH, 'tests', 'aux_results')
save_dir = os.path.join(PROJECT_PATH, 'tests', 'aux_data')
utils.check_path(results_dir)
utils.check_path(save_dir)
utils.check_path(os.path.join(results_dir, dataset_name))
# load-save data
normal_data_path = os.path.join(
save_dir, 'normal_data_%s_%s_loading.pkl' % (dataset_name, tf_version))
if os.path.exists(normal_data_path):
(x_train,
y_train), (x_val, y_val), (x_test,
y_test) = pd.read_pickle(normal_data_path)
else:
(x_train, y_train), (x_val, y_val), (x_test,
y_test) = loader(return_val=True)
normal_data = (x_train, y_train), (x_val, y_val), (x_test, y_test)
utils.save_pickle(normal_data, normal_data_path)
# create model
# n, k = (10, 4)
# mdl = create_wide_residual_network(
# x_train.shape[1:], transformer.n_transforms, n, k)
mdl.compile('adam', 'categorical_crossentropy', ['acc'])
# selec inliers
x_train = x_train[y_train.flatten() == single_class_ind]
x_val = x_val[y_val.flatten() == single_class_ind]
# load-save transformed data
transformed_data_path = os.path.join(
save_dir, '%s_data_%s_%s_loading.pkl' %
(transformer_name, dataset_name, tf_version))
if os.path.exists(transformed_data_path):
(x_train_transform_tf1,
y_train_transform_tf1), (x_val_transform_tf1, y_val_transform_tf1), (
x_test_transform_tf1,
y_test_transform_tf1) = pd.read_pickle(transformed_data_path)
else:
# transform all data
y_train_transform_tf1 = np.tile(np.arange(transformer.n_transforms),
len(x_train))
x_train_transform_tf1 = transformer.transform_batch(
np.repeat(x_train, transformer.n_transforms, axis=0),
y_train_transform_tf1)
y_val_transform_tf1 = np.tile(np.arange(transformer.n_transforms),
len(x_val))
x_val_transform_tf1 = transformer.transform_batch(
np.repeat(x_val, transformer.n_transforms, axis=0),
y_val_transform_tf1)
y_test_transform_tf1 = np.tile(np.arange(transformer.n_transforms),
len(x_test))
x_test_transform_tf1 = transformer.transform_batch(
np.repeat(x_test, transformer.n_transforms, axis=0),
y_test_transform_tf1)
transformed_data = ((x_train_transform_tf1, y_train_transform_tf1),
(x_val_transform_tf1, y_val_transform_tf1),
(x_test_transform_tf1, y_test_transform_tf1))
utils.save_pickle(transformed_data, transformed_data_path)
print(x_train.shape)
print(x_train_transform_tf1.shape)
print(x_test.shape)
print(x_test_transform_tf1.shape)
# train model
batch_size = 128
if epochs is None:
epochs = int(np.ceil(200 / transformer.n_transforms))
mdl.fit(x=x_train_transform_tf1,
y=to_categorical(y_train_transform_tf1),
batch_size=batch_size,
epochs=epochs)
scores = np.zeros((len(x_test), ))
matrix_evals = np.zeros(
(len(x_test), transformer.n_transforms, transformer.n_transforms))
x_pred_train = mdl.predict(x_train_transform_tf1, batch_size=1024)
x_pred_test = mdl.predict(x_test_transform_tf1, batch_size=1024)
print(x_pred_train.shape)
print(x_pred_test.shape)
for t_ind in range(transformer.n_transforms):
ind_x_pred_equal_to_t_ind = np.where(y_train_transform_tf1 == t_ind)[0]
observed_dirichlet = x_pred_train[ind_x_pred_equal_to_t_ind]
log_p_hat_train = np.log(observed_dirichlet).mean(axis=0)
alpha_sum_approx = calc_approx_alpha_sum(observed_dirichlet)
alpha_0 = observed_dirichlet.mean(axis=0) * alpha_sum_approx
mle_alpha_t = fixed_point_dirichlet_mle(alpha_0, log_p_hat_train)
ind_x_pred_test_equal_to_t_ind = np.where(
y_test_transform_tf1 == t_ind)[0]
x_test_p = x_pred_test[ind_x_pred_test_equal_to_t_ind]
matrix_evals[:, :, t_ind] += x_test_p
scores += dirichlet_normality_score(mle_alpha_t, x_test_p)
scores /= transformer.n_transforms
matrix_evals /= transformer.n_transforms
scores_simple = np.trace(matrix_evals, axis1=1, axis2=2)
scores_entropy = -1 * get_entropy(matrix_evals)
scores_xH = -1 * get_xH(transformer, matrix_evals)
labels = y_test.flatten() == single_class_ind
save_results_file(results_dir,
dataset_name,
single_class_ind,
scores=scores,
labels=labels,
experiment_name='%s-%s-loading-%s' %
(model_name, transformer_name, tf_version))
save_results_file(results_dir,
dataset_name,
single_class_ind,
scores=scores_simple,
labels=labels,
experiment_name='%s-%s-simple-loading-%s' %
(model_name, transformer_name, tf_version))
save_results_file(results_dir,
dataset_name,
single_class_ind,
scores=scores_entropy,
labels=labels,
experiment_name='%s-%s-entropy-loading-%s' %
(model_name, transformer_name, tf_version))
save_results_file(results_dir,
dataset_name,
single_class_ind,
scores=scores_xH,
labels=labels,
experiment_name='%s-%s-xH-loading-%s' %
(model_name, transformer_name, tf_version))
mdl_weights_name = '{}_{}_{}_{}_loading_{}_weights.h5'.format(
model_name, transformer_name, dataset_name, tf_version,
get_class_name_from_index(single_class_ind, dataset_name))
mdl_weights_path = os.path.join(results_dir, dataset_name,
mdl_weights_name)
mdl.save_weights(mdl_weights_path)
reset_weights()
"""
Time test_model_original(transformer, load_hits4c, dataset_name='hits-4-c', tf_version='tf1') 00:04:31.65
(0.992217, 0.9895665, 0.99131725, 0.989478125)
(0.99240075, 0.9900822499999999, 0.99215325, 0.9901300000000001)
"""
return get_roc_auc(scores, labels), get_roc_auc(scores_simple, labels), \
get_roc_auc(scores_entropy, labels), get_roc_auc(scores_xH, labels)
def _kernal_plus_transformations_experiment(dataset_load_fn, dataset_name,
single_class_ind, gpu_q):
# gpu_to_use = gpu_q.get()
# os.environ["CUDA_VISIBLE_DEVICES"] = gpu_to_use
(x_train, y_train), (x_test, y_test) = dataset_load_fn()
if dataset_name in ['cats-vs-dogs']:
transformer = None
else:
transformer = PlusKernelTransformer(translation_x=8,
translation_y=8,
rotations=1,
flips=1,
gauss=1,
log=1)
n, k = (10, 4)
mdl = create_wide_residual_network(x_train.shape[1:],
transformer.n_transforms, n, k)
mdl.compile('adam', 'categorical_crossentropy', ['acc'])
# get inliers of specific class
x_train_task = x_train[y_train.flatten() == single_class_ind]
# [0_i, ..., (N_transforms-1)_i, ..., ..., 0_N_samples, ...,
# (N_transforms-1)_N_samples] shape: (N_transforms*N_samples,)
transformations_inds = np.tile(np.arange(transformer.n_transforms),
len(x_train_task))
x_train_task_transformed = transformer.transform_batch(
np.repeat(x_train_task, transformer.n_transforms, axis=0),
transformations_inds)
batch_size = 128
mdl.fit(
x=x_train_task_transformed,
y=to_categorical(transformations_inds),
batch_size=batch_size,
epochs=2 #int(np.ceil(200/transformer.n_transforms))
)
scores = np.zeros((len(x_test), ))
matrix_evals = np.zeros(
(len(x_test), transformer.n_transforms, transformer.n_transforms))
observed_data = x_train_task
for t_ind in range(transformer.n_transforms):
observed_dirichlet = mdl.predict(transformer.transform_batch(
observed_data, [t_ind] * len(observed_data)),
batch_size=1024)
log_p_hat_train = np.log(observed_dirichlet).mean(axis=0)
alpha_sum_approx = calc_approx_alpha_sum(observed_dirichlet)
alpha_0 = observed_dirichlet.mean(axis=0) * alpha_sum_approx
mle_alpha_t = fixed_point_dirichlet_mle(alpha_0, log_p_hat_train)
x_test_p = mdl.predict(transformer.transform_batch(
x_test, [t_ind] * len(x_test)),
batch_size=1024)
matrix_evals[:, :, t_ind] += x_test_p
scores += dirichlet_normality_score(mle_alpha_t, x_test_p)
scores /= transformer.n_transforms
matrix_evals /= transformer.n_transforms
scores_simple = np.trace(matrix_evals, axis1=1, axis2=2)
scores_entropy = -1 * get_entropy(matrix_evals)
scores_xH = -1 * get_xH(transformer, matrix_evals)
labels = y_test.flatten() == single_class_ind
save_results_file(dataset_name,
single_class_ind,
scores=scores,
labels=labels,
experiment_name='kernel-plus-transformations')
save_results_file(dataset_name,
single_class_ind,
scores=scores_simple,
labels=labels,
experiment_name='kernel-plus-transformations-simple')
save_results_file(dataset_name,
single_class_ind,
scores=scores_entropy,
labels=labels,
experiment_name='kernel-plus-transformations-entropy')
save_results_file(dataset_name,
single_class_ind,
scores=scores_xH,
labels=labels,
experiment_name='kernel-plus-transformations-xH')
mdl_weights_name = '{}_kernel-plus-transformations_{}_{}_weights.h5'.format(
dataset_name, get_class_name_from_index(single_class_ind,
dataset_name),
datetime.datetime.now().strftime('%Y-%m-%d-%H%M'))
mdl_weights_path = os.path.join(RESULTS_DIR, dataset_name,
mdl_weights_name)
mdl.save_weights(mdl_weights_path)
