class Experiment:
PCA_COMPRESS_WORD_DIM = 100
PCA_COMPRESS_IMG_DIM = 100
SEED_NUM = None
MAX_DIM = 300
MIN_DIM = 10
DIM_STEP = 10
CONFIG_YAML = 'config.yml'
def __init__(self, line_flag=False):
# log setting
program = os.path.basename(__name__)
self.logger = logging.getLogger(program)
logging.basicConfig(
format='%(asctime)s : %(name)s : %(levelname)s : %(message)s')
# load config file
f = open(Experiment.CONFIG_YAML, 'r')
self.config = yaml.load(f)
f.close()
self.english_corpus_dir = self.config['english_corpus_dir']
self.japanese_corpus_dir = self.config['japanese_corpus_dir']
self.japanese_original_corpus_dir = self.config[
'japanese_original_corpus_dir']
self.img_features_npy = self.config['img_features_npy']
self.img_original_dir = self.config['img_original_dir']
self.img_correspondence_path = self.config['img_correspondence_path']
self.joint = Joint(self.english_corpus_dir, self.img_features_npy,
self.japanese_corpus_dir, self.img_original_dir,
self.img_correspondence_path,
self.japanese_original_corpus_dir,
Experiment.PCA_COMPRESS_WORD_DIM,
Experiment.PCA_COMPRESS_IMG_DIM, line_flag)
self.logger.info("<Initilalizing Experiment>")
if Experiment.SEED_NUM is not None:
self.logger.info("seed: %s", Experiment.SEED_NUM)
np.random.seed(Experiment.SEED_NUM)
def process_features(self):
self.joint.create_features()
self.joint.pca_train_and_test_data()
def fit_changing_sample_num(self, sample_num_list):
data_num = self.joint.english_feature.get_train_data_num()
for s in sample_num_list:
sampled_indices = feat.BaseFeature.sample_indices(data_num, s)
self.joint.gcca_fit(s, 0.1, sampled_indices)
self.joint.cca_fit(s, 0.1, sampled_indices)
def calc_accuracy(self, start_dim=1, end_dim=100, dim_step=1):
res_cca_list = []
res_gcca_list = []
print "|dim|CCA|GCCA|"
for i in xrange(start_dim, end_dim, dim_step):
res_cca = self.cca_calc_search_precision(i)
res_gcca = self.gcca_calc_search_precision(i)
print "|%d|%f|%f|" % (i, res_cca, res_gcca)
res_cca_list.append(res_cca)
res_gcca_list.append(res_gcca)
return res_cca_list, res_gcca_list
def plot_result(self, sample_num=500, reg_param=0.1):
self.joint.gcca_transform(sample_num, reg_param)
self.joint.cca_transform(sample_num, reg_param)
self.joint.cca_plot()
self.joint.gcca_plot()
def calc_accuracy_changing_sample_num(self,
sample_num_list,
reg_param=0.1):
res_cca_data = []
res_gcca_data = []
for sample_num in sample_num_list:
self.joint.gcca_transform(sample_num, reg_param)
self.joint.cca_transform(sample_num, reg_param)
res_cca_list, res_gcca_list = self.calc_accuracy(
Experiment.MIN_DIM, Experiment.MAX_DIM + 1,
Experiment.DIM_STEP)
res_cca_data.append(res_cca_list)
res_gcca_data.append(res_gcca_list)
res_cca_arr = np.array(res_cca_data)
res_gcca_arr = np.array(res_gcca_data)
# np.save('output/results/res_cca_arr.npy', res_cca_arr)
# np.save('output/results/res_gcca_arr.npy', res_gcca_arr)
# joint.gcca_transform(mode='PART', line_flag=True, step=5)
# res_cca_arr = np.load('output/results/res_cca_arr.npy')
# res_gcca_arr = np.load('output/results/res_gcca_arr.npy')
def fit_chenging_regparam(self, reg_params, sample_num=500):
data_num = self.joint.english_feature.get_train_data_num()
for r in reg_params:
sampled_indices = feat.BaseFeature.sample_indices(
data_num, sample_num)
self.joint.gcca_fit(sample_num, r, sampled_indices)
self.joint.cca_fit(sample_num, r, sampled_indices)
def calc_accuracy_changing_reg_params(self,
sample_num,
reg_list,
col_num=5):
res_cca_data = []
res_gcca_data = []
for reg in reg_list:
self.joint.gcca_transform(sample_num, reg)
self.joint.cca_transform(sample_num, reg)
res_cca_list, res_gcca_list = self.calc_accuracy(
Experiment.MIN_DIM, Experiment.MAX_DIM + 1,
Experiment.DIM_STEP)
res_cca_data.append(res_cca_list)
res_gcca_data.append(res_gcca_list)
res_cca_arr = np.array(res_cca_data)
res_gcca_arr = np.array(res_gcca_data)
np.save('output/results/res_cca_reg_arr.npy', res_cca_arr)
np.save('output/results/res_gcca_reg_arr.npy', res_gcca_arr)
# joint.gcca_transform(line_flag=True, step=5)
# res_cca_arr = np.load('output/results/res_cca_reg_arr.npy')
# res_gcca_arr = np.load('output/results/res_gcca_reg_arr.npy')
self.plot_results(res_cca_arr, res_gcca_arr, reg_list, col_num, 'REG')
def plot_original_data(self):
self.joint.plot_original_data()
def cca_calc_search_precision(self, min_dim, neighbor_num=1):
en_mat, jp_mat = self.joint.cca.z_list[
0][:, :min_dim], self.joint.cca.z_list[1][:, :min_dim]
nn = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(en_mat)
dists, nn_indices = nn.kneighbors(jp_mat,
neighbor_num,
return_distance=True)
hit_count = 0
for j_idx, nn_indices_row in enumerate(nn_indices):
# print nn_indices_row
if j_idx in nn_indices_row:
# print True
hit_count += 1
else:
pass
# print False
return float(hit_count) / len(nn_indices) * 100
def gcca_calc_search_precision(self, min_dim, neighbor_num=1):
en_mat, im_mat, jp_mat = self.joint.gcca.z_list[
0][:, :min_dim], self.joint.gcca.z_list[
1][:, :min_dim], self.joint.gcca.z_list[2][:, :min_dim]
nn = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(en_mat)
dists, nn_indices = nn.kneighbors(jp_mat,
neighbor_num,
return_distance=True)
hit_count = 0
for j_idx, nn_indices_row in enumerate(nn_indices):
# print nn_indices_row
if j_idx in nn_indices_row:
# print True
hit_count += 1
else:
# print False
pass
return float(hit_count) / len(nn_indices) * 100
def plot_results(self,
res_cca,
res_gcca,
title_list,
col_num=2,
mode='SAMPLE'):
data_num = len(res_cca)
row_num = data_num / col_num
if row_num - float(data_num) / col_num != 0:
print row_num
row_num = row_num + 1
fig = plt.figure()
# plt.title('Accuracy')
for i, (title, row_cca,
row_gcca) in enumerate(zip(title_list, res_cca, res_gcca)):
plt.subplot(row_num, col_num, i + 1)
plt.plot(np.arange(len(row_cca)) * 10 + 10, row_cca, '-r')
plt.plot(np.arange(len(row_gcca)) * 10 + 10, row_gcca, '-b')
x_min, x_max = plt.gca().get_xlim()
y_min, y_max = plt.gca().get_ylim()
if mode == 'SAMPLE':
plt.text(0.5 * (x_min + x_max),
0.5 * (y_min + y_max),
'sample:%d' % title,
ha='center',
va='center',
color='gray')
elif mode == 'REG':
plt.text(0.5 * (x_min + x_max),
0.5 * (y_min + y_max),
'reg:%s' % title,
ha='center',
va='center',
color='gray')
plt.tight_layout()
plt.show()
class Experiment:
PCA_COMPRESS_WORD_DIM = 100
PCA_COMPRESS_IMG_DIM = 100
SEED_NUM = None
MAX_DIM = 300
MIN_DIM = 10
DIM_STEP = 10
CONFIG_YAML = 'config.yml'
def __init__(self, line_flag=False):
# log setting
program = os.path.basename(__name__)
self.logger = logging.getLogger(program)
logging.basicConfig(format='%(asctime)s : %(name)s : %(levelname)s : %(message)s')
# load config file
f = open(Experiment.CONFIG_YAML, 'r')
self.config = yaml.load(f)
f.close()
self.english_corpus_dir = self.config['english_corpus_dir']
self.japanese_corpus_dir = self.config['japanese_corpus_dir']
self.japanese_original_corpus_dir = self.config['japanese_original_corpus_dir']
self.img_features_npy = self.config['img_features_npy']
self.img_original_dir = self.config['img_original_dir']
self.img_correspondence_path = self.config['img_correspondence_path']
self.joint = Joint(
self.english_corpus_dir,
self.img_features_npy,
self.japanese_corpus_dir,
self.img_original_dir,
self.img_correspondence_path,
self.japanese_original_corpus_dir,
Experiment.PCA_COMPRESS_WORD_DIM,
Experiment.PCA_COMPRESS_IMG_DIM,
line_flag
)
self.logger.info("<Initilalizing Experiment>")
if Experiment.SEED_NUM is not None:
self.logger.info("seed: %s" , Experiment.SEED_NUM)
np.random.seed(Experiment.SEED_NUM)
def process_features(self):
self.joint.create_features()
self.joint.pca_train_and_test_data()
def fit_changing_sample_num(self, sample_num_list):
data_num = self.joint.english_feature.get_train_data_num()
for s in sample_num_list:
sampled_indices = feat.BaseFeature.sample_indices(data_num, s)
self.joint.gcca_fit(s, 0.1, sampled_indices)
self.joint.cca_fit(s, 0.1, sampled_indices)
def calc_accuracy(self, start_dim=1, end_dim=100, dim_step=1):
res_cca_list = []
res_gcca_list = []
print "|dim|CCA|GCCA|"
for i in xrange(start_dim, end_dim, dim_step):
res_cca = self.cca_calc_search_precision(i)
res_gcca = self.gcca_calc_search_precision(i)
print "|%d|%f|%f|" % (i, res_cca, res_gcca)
res_cca_list.append(res_cca)
res_gcca_list.append(res_gcca)
return res_cca_list, res_gcca_list
def plot_result(self, sample_num=500, reg_param=0.1):
self.joint.gcca_transform(sample_num, reg_param)
self.joint.cca_transform(sample_num, reg_param)
self.joint.cca_plot()
self.joint.gcca_plot()
def calc_accuracy_changing_sample_num(self, sample_num_list, reg_param=0.1):
res_cca_data = []
res_gcca_data = []
for sample_num in sample_num_list:
self.joint.gcca_transform(sample_num, reg_param)
self.joint.cca_transform(sample_num, reg_param)
res_cca_list, res_gcca_list = self.calc_accuracy(Experiment.MIN_DIM, Experiment.MAX_DIM + 1, Experiment.DIM_STEP)
res_cca_data.append(res_cca_list)
res_gcca_data.append(res_gcca_list)
res_cca_arr = np.array(res_cca_data)
res_gcca_arr = np.array(res_gcca_data)
# np.save('output/results/res_cca_arr.npy', res_cca_arr)
# np.save('output/results/res_gcca_arr.npy', res_gcca_arr)
# joint.gcca_transform(mode='PART', line_flag=True, step=5)
# res_cca_arr = np.load('output/results/res_cca_arr.npy')
# res_gcca_arr = np.load('output/results/res_gcca_arr.npy')
def fit_chenging_regparam(self, reg_params, sample_num=500):
data_num = self.joint.english_feature.get_train_data_num()
for r in reg_params:
sampled_indices = feat.BaseFeature.sample_indices(data_num, sample_num)
self.joint.gcca_fit(sample_num, r, sampled_indices)
self.joint.cca_fit(sample_num, r, sampled_indices)
def calc_accuracy_changing_reg_params(self, sample_num, reg_list, col_num=5):
res_cca_data = []
res_gcca_data = []
for reg in reg_list:
self.joint.gcca_transform(sample_num,reg)
self.joint.cca_transform(sample_num, reg)
res_cca_list, res_gcca_list = self.calc_accuracy(Experiment.MIN_DIM, Experiment.MAX_DIM + 1 , Experiment.DIM_STEP)
res_cca_data.append(res_cca_list)
res_gcca_data.append(res_gcca_list)
res_cca_arr = np.array(res_cca_data)
res_gcca_arr = np.array(res_gcca_data)
np.save('output/results/res_cca_reg_arr.npy', res_cca_arr)
np.save('output/results/res_gcca_reg_arr.npy', res_gcca_arr)
# joint.gcca_transform(line_flag=True, step=5)
# res_cca_arr = np.load('output/results/res_cca_reg_arr.npy')
# res_gcca_arr = np.load('output/results/res_gcca_reg_arr.npy')
self.plot_results(res_cca_arr, res_gcca_arr, reg_list, col_num, 'REG')
def plot_original_data(self):
self.joint.plot_original_data()
def cca_calc_search_precision(self, min_dim, neighbor_num=1):
en_mat, jp_mat = self.joint.cca.z_list[0][:, :min_dim], self.joint.cca.z_list[1][:, :min_dim]
nn = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(en_mat)
dists, nn_indices = nn.kneighbors(jp_mat, neighbor_num, return_distance=True)
hit_count = 0
for j_idx, nn_indices_row in enumerate(nn_indices):
# print nn_indices_row
if j_idx in nn_indices_row:
# print True
hit_count += 1
else:
pass
# print False
return float(hit_count) / len(nn_indices) * 100
def gcca_calc_search_precision(self, min_dim, neighbor_num=1):
en_mat, im_mat, jp_mat = self.joint.gcca.z_list[0][:, :min_dim], self.joint.gcca.z_list[1][:, :min_dim], self.joint.gcca.z_list[2][:, :min_dim]
nn = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(en_mat)
dists, nn_indices = nn.kneighbors(jp_mat, neighbor_num, return_distance=True)
hit_count = 0
for j_idx, nn_indices_row in enumerate(nn_indices):
# print nn_indices_row
if j_idx in nn_indices_row:
# print True
hit_count += 1
else:
# print False
pass
return float(hit_count) / len(nn_indices) * 100
def plot_results(self, res_cca, res_gcca, title_list, col_num=2, mode='SAMPLE'):
data_num = len(res_cca)
row_num = data_num / col_num
if row_num - float(data_num)/col_num != 0:
print row_num
row_num = row_num + 1
fig = plt.figure()
# plt.title('Accuracy')
for i, (title, row_cca, row_gcca) in enumerate(zip(title_list, res_cca, res_gcca)):
plt.subplot(row_num , col_num, i + 1)
plt.plot(np.arange(len(row_cca)) * 10 + 10, row_cca, '-r')
plt.plot(np.arange(len(row_gcca)) * 10 + 10, row_gcca, '-b')
x_min, x_max = plt.gca().get_xlim()
y_min, y_max = plt.gca().get_ylim()
if mode == 'SAMPLE':
plt.text(0.5 * (x_min + x_max), 0.5 * (y_min + y_max), 'sample:%d' % title, ha='center', va='center', color='gray')
elif mode == 'REG':
plt.text(0.5 * (x_min + x_max), 0.5 * (y_min + y_max), 'reg:%s' % title, ha='center', va='center', color='gray')
plt.tight_layout()
plt.show()
__author__ = 'rupy'
import logging
from joint import Joint
if __name__ == "__main__":
logging.root.setLevel(level=logging.INFO)
english_corpus_dir = '../PascalSentenceDataset/english/'
japanese_corpus_dir = '../PascalSentenceDataset/line_wakati/'
japanese_original_corpus_dir = '../PascalSentenceDataset/japanese/'
img_features_npy = 'pascal_features.npy'
img_original_dir = '../PascalSentenceDataset/dataset/'
img_correspondence_path = "../PascalSentenceDataset/correspondence.csv"
joint = Joint(english_corpus_dir, img_features_npy, japanese_corpus_dir,
img_original_dir, img_correspondence_path,
japanese_original_corpus_dir)
# retrieval
joint.create_features()
joint.pca_train_and_test_data()
joint.cca_transform(line_flag=False, step=1, reg_param=0.1)
joint.cca_plot()
joint.gcca_transform(line_flag=False, step=1, reg_param=0.1)
joint.gcca_plot()
# joint.retrieval_j2e_by_cca(3)
joint.retrieval_j2e_by_gcca(3)
# joint.retrieval_j2i_by_gcca(3)
if __name__=="__main__":
logging.root.setLevel(level=logging.INFO)
english_corpus_dir = '../PascalSentenceDataset/english/'
japanese_corpus_dir = '../PascalSentenceDataset/line_wakati/'
japanese_original_corpus_dir = '../PascalSentenceDataset/japanese/'
img_features_npy = 'pascal_features.npy'
img_original_dir = '../PascalSentenceDataset/dataset/'
img_correspondence_path = "../PascalSentenceDataset/correspondence.csv"
joint = Joint(
english_corpus_dir,
img_features_npy,
japanese_corpus_dir,
img_original_dir,
img_correspondence_path,
japanese_original_corpus_dir
)
# retrieval
joint.create_features()
joint.pca_train_and_test_data()
joint.cca_transform(line_flag=False, step=1, reg_param=0.1)
joint.cca_plot()
joint.gcca_transform(line_flag=False, step=1, reg_param=0.1)
joint.gcca_plot()
# joint.retrieval_j2e_by_cca(3)
joint.retrieval_j2e_by_gcca(3)
# joint.retrieval_j2i_by_gcca(3)
