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New translations kernel_knn_cv.py (Chinese Simplified)

l10n_v0.2.x
linlin 4 years ago
parent
954a660e27
commit
a788e6ade5
1 changed files with 25 additions and 25 deletions
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      lang/zh/gklearn/preimage/kernel_knn_cv.py

+ 25
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lang/zh/gklearn/preimage/kernel_knn_cv.py View File

@@ -33,35 +33,35 @@ def kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_opti
if save_results:
# create result files.
print('creating output files...')
fn_output_detail, fn_output_summary = __init_output_file_knn(ds_name, kernel_options['name'], mpg_options['fit_method'], dir_save)
fn_output_detail, fn_output_summary = _init_output_file_knn(ds_name, kernel_options['name'], mpg_options['fit_method'], dir_save)
else:
fn_output_detail, fn_output_summary = None, None
# 2. compute/load Gram matrix a priori.
print('2. computing/loading Gram matrix...')
gram_matrix_unnorm, time_precompute_gm = __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all)
gram_matrix_unnorm, time_precompute_gm = _get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all)
# 3. perform k-nn CV.
print('3. performing k-nn CV...')
if train_examples == 'k-graphs' or train_examples == 'expert' or train_examples == 'random':
__kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary)
_kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary)
elif train_examples == 'best-dataset':
__kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary)
_kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary)
elif train_examples == 'trainset':
__kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary)
_kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary)

print('\ncomplete.\n')
def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary):
def _kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary):
Gn = dataset_all.graphs
y_all = dataset_all.targets
n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size']

# get shuffles.
train_indices, test_indices, train_nums, y_app = __get_shuffles(y_all, n_splits, test_size)
train_indices, test_indices, train_nums, y_app = _get_shuffles(y_all, n_splits, test_size)
accuracies = [[], [], []]
for trial in range(len(train_indices)):
@@ -89,11 +89,11 @@ def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kerne
mge_options['update_order'] = True
mpg_options['gram_matrix_unnorm'] = gm_unnorm_trial[i_start:i_end,i_start:i_end].copy()
mpg_options['runtime_precompute_gm'] = 0
set_median, gen_median_uo = __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options)
set_median, gen_median_uo = _generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options)
mge_options['update_order'] = False
mpg_options['gram_matrix_unnorm'] = gm_unnorm_trial[i_start:i_end,i_start:i_end].copy()
mpg_options['runtime_precompute_gm'] = 0
_, gen_median = __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options)
_, gen_median = _generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options)
medians[0].append(set_median)
medians[1].append(gen_median)
medians[2].append(gen_median_uo)
@@ -104,10 +104,10 @@ def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kerne
# compute dis_mat between medians.
dataset = dataset_all.copy()
dataset.load_graphs([g.copy() for g in G_app], targets=None)
gm_app_unnorm, _ = __compute_gram_matrix_unnorm(dataset, kernel_options.copy())
gm_app_unnorm, _ = _compute_gram_matrix_unnorm(dataset, kernel_options.copy())
# compute the entire Gram matrix.
graph_kernel = __get_graph_kernel(dataset.copy(), kernel_options.copy())
graph_kernel = _get_graph_kernel(dataset.copy(), kernel_options.copy())
kernels_to_medians = []
for g in G_app:
kernels_to_median, _ = graph_kernel.compute(g, G_test, **kernel_options.copy())
@@ -161,13 +161,13 @@ def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kerne
f_summary.close()
def __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary):
def _kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary):
Gn = dataset_all.graphs
y_all = dataset_all.targets
n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size']

# get shuffles.
train_indices, test_indices, train_nums, y_app = __get_shuffles(y_all, n_splits, test_size)
train_indices, test_indices, train_nums, y_app = _get_shuffles(y_all, n_splits, test_size)
accuracies = []
for trial in range(len(train_indices)):
@@ -204,10 +204,10 @@ def __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, g
# compute dis_mat between medians.
dataset = dataset_all.copy()
dataset.load_graphs([g.copy() for g in best_graphs], targets=None)
gm_app_unnorm, _ = __compute_gram_matrix_unnorm(dataset, kernel_options.copy())
gm_app_unnorm, _ = _compute_gram_matrix_unnorm(dataset, kernel_options.copy())
# compute the entire Gram matrix.
graph_kernel = __get_graph_kernel(dataset.copy(), kernel_options.copy())
graph_kernel = _get_graph_kernel(dataset.copy(), kernel_options.copy())
kernels_to_best_graphs = []
for g in best_graphs:
kernels_to_best_graph, _ = graph_kernel.compute(g, G_test, **kernel_options.copy())
@@ -259,7 +259,7 @@ def __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, g
f_summary.close()
def __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary):
def _kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary):
y_all = dataset_all.targets
n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size']
@@ -268,7 +268,7 @@ def __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options,
dis_mat, _, _, _ = compute_distance_matrix(gram_matrix)

# get shuffles.
train_indices, test_indices, _, _ = __get_shuffles(y_all, n_splits, test_size)
train_indices, test_indices, _, _ = _get_shuffles(y_all, n_splits, test_size)
accuracies = []
for trial in range(len(train_indices)):
@@ -317,7 +317,7 @@ def __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options,
f_summary.close()
def __get_shuffles(y_all, n_splits, test_size):
def _get_shuffles(y_all, n_splits, test_size):
rs = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=0)
train_indices = [[] for _ in range(n_splits)]
test_indices = [[] for _ in range(n_splits)]
@@ -335,7 +335,7 @@ def __get_shuffles(y_all, n_splits, test_size):
return train_indices, test_indices, train_nums, keys
def __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options):
def _generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options):
mpg = MedianPreimageGenerator()
mpg.dataset = dataset.copy()
mpg.set_options(**mpg_options.copy())
@@ -346,7 +346,7 @@ def __generate_median_preimages(dataset, mpg_options, kernel_options, ged_option
return mpg.set_median, mpg.gen_median


def __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all):
def _get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all):
if load_gm == 'auto':
gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz'
gmfile_exist = os.path.isfile(os.path.abspath(gm_fname))
@@ -355,10 +355,10 @@ def __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all):
gram_matrix_unnorm = gmfile['gram_matrix_unnorm']
time_precompute_gm = float(gmfile['run_time'])
else:
gram_matrix_unnorm, time_precompute_gm = __compute_gram_matrix_unnorm(dataset_all, kernel_options)
gram_matrix_unnorm, time_precompute_gm = _compute_gram_matrix_unnorm(dataset_all, kernel_options)
np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm=gram_matrix_unnorm, run_time=time_precompute_gm)
elif not load_gm:
gram_matrix_unnorm, time_precompute_gm = __compute_gram_matrix_unnorm(dataset_all, kernel_options)
gram_matrix_unnorm, time_precompute_gm = _compute_gram_matrix_unnorm(dataset_all, kernel_options)
np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm=gram_matrix_unnorm, run_time=time_precompute_gm)
else:
gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz'
@@ -369,7 +369,7 @@ def __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all):
return gram_matrix_unnorm, time_precompute_gm


def __get_graph_kernel(dataset, kernel_options):
def _get_graph_kernel(dataset, kernel_options):
from gklearn.utils.utils import get_graph_kernel_by_name
graph_kernel = get_graph_kernel_by_name(kernel_options['name'],
node_labels=dataset.node_labels,
@@ -381,7 +381,7 @@ def __get_graph_kernel(dataset, kernel_options):
return graph_kernel
def __compute_gram_matrix_unnorm(dataset, kernel_options):
def _compute_gram_matrix_unnorm(dataset, kernel_options):
from gklearn.utils.utils import get_graph_kernel_by_name
graph_kernel = get_graph_kernel_by_name(kernel_options['name'],
node_labels=dataset.node_labels,
@@ -397,7 +397,7 @@ def __compute_gram_matrix_unnorm(dataset, kernel_options):
return gram_matrix_unnorm, run_time
def __init_output_file_knn(ds_name, gkernel, fit_method, dir_output):
def _init_output_file_knn(ds_name, gkernel, fit_method, dir_output):
if not os.path.exists(dir_output):
os.makedirs(dir_output)
fn_output_detail = 'results_detail_knn.' + ds_name + '.' + gkernel + '.csv'


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