def test_lasso_regression():
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
from sklearn.linear_model import Lasso
model = Lasso(alpha=1e-3)
model.fit(viper.train_feat, viper.train_y)
y_pred = model.predict(viper.test_feat)
print 'testing error {}'.format(abs_error(y_pred, viper.test_y))
def test_SVR():
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
from sklearn.svm import SVR
model = SVR(C=10, kernel='rbf', shrinking=False, verbose=True)
model.fit(viper.train_feat, viper.train_y)
y_pred = model.predict(viper.test_feat)
print 'testing error {}'.format(abs_error(y_pred, viper.test_y))
def test_linear_regression():
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
from sklearn.linear_model import LinearRegression
model = LinearRegression(normalize=True)
model.fit(viper.train_feat, viper.train_y)
y_pred = model.predict(viper.test_feat)
print 'testing error {}'.format(abs_error(y_pred, viper.test_y))
def test_knn_regression():
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
from sklearn.neighbors import KNeighborsRegressor
model = KNeighborsRegressor(n_neighbors=5, weights='uniform', metric='euclidean')
model.fit(viper.train_feat, viper.train_y)
n_test = len(viper.test_feat)
y_pred = np.zeros(n_test)
for i, feat in zip(np.arange(n_test), viper.test_feat):
dist, ind = model.kneighbors(feat)
y_pred[i] = (viper.train_y[ind]*np.exp(-dist**2)).sum()/(np.exp(-dist**2)).sum()
# y_pred = model.predict(viper.test_feat)
print 'testing error {}'.format(abs_error(y_pred, viper.test_y))
def main():
''' pipeline for evaluating salience '''
# three types:
# 1) unsupervised, knnsal
# 2) groundtruth, gtsal
# 3) prediction, predsal
## load cnn salience with groundtruth
supsal_path = '../data_viper/model_feat/salmaps_comparison.pkl'
test_imids, salmap_gt, salmap_pred = loadfile(supsal_path)
mapsz = salmap_gt[0].shape
## load knn salience
knnsal_path = '../data_viper/salience_all.mat'
tmp = loadfile(knnsal_path)
knn_gal = tmp['salience_all_gal'] # view a
knn_prb = tmp['salience_all_gal'] # view b
labeled_imidx_path = '../data_viper/labeled_imidx.mat'
tmp = loadfile(labeled_imidx_path)
labeled_imidx = tmp['labeled_imidx'].flatten()
salmap_knn_small = knn_gal[:, :, labeled_imidx].transpose((2, 0, 1))
salmap_knn_all = [mapresize(im, size=mapsz) for im in salmap_knn_small]
# get rid of background for better illustration
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
salmap_knn = []
for seg, msk in zip(viper.segmsks, salmap_knn_all):
idx = seg == 0
msk[idx] = 0
salmap_knn.append(msk)
salmap_knn = np.asarray(salmap_knn)
# qualitative evaluation
save_path = '../data_viper/model_feat/'
for i in test_imids:
pl.figure(1)
pl.subplot(1, 4, 1) # show image
pl.imshow(viper.imgs[i])
pl.title('image')
pl.subplot(1, 4, 2) # show groundtruth salience
pl.imshow(salmap_gt[i]*255., cmap='hot', vmin=0, vmax=255)
pl.title('groundtruth')
pl.subplot(1, 4, 3) # show knn salience
pl.imshow(salmap_knn[i]*255., cmap='hot', vmin=0, vmax=255)
pl.title('KNN salience')
pl.xlabel('abserr={0:.2f}'.format(abs_error(salmap_knn[i].flatten(), salmap_gt[i].flatten())))
pl.subplot(1, 4, 4) # show CNN prediction salience
pl.imshow(salmap_pred[i]*255., cmap='hot', vmin=0, vmax=255)
pl.title('CNN salience')
pl.xlabel('abserr={0:.2f}'.format(abs_error(salmap_pred[i].flatten(), salmap_gt[i].flatten())))
pl.savefig(save_path + '{0:03d}.jpg'.format(i))
print save_path +'{0:03d}.jpg'.format(i) + ' saved!'
# quantitative evaluation
test_idx = np.unique(test_imids)
print 'mean abs error - KNN vs Gt: {0:.2f}'.format(abs_error(salmap_knn[test_idx], salmap_gt[test_idx]))
print 'mean abs error - CNN vs Gt: {0:.2f}'.format(abs_error(salmap_pred[test_idx], salmap_gt[test_idx]))
# pl.figure(2)
# test_idx = np.unique(test_imids)
# recall_knn, precision_knn = get_roc_curve(salmap_gt[test_idx], salmap_knn[test_idx])
# pl.plot(recall_knn, precision_knn, 'b', linewidth=2, label='knn vs. gt')
# recall_cnn, precision_cnn = get_roc_curve(salmap_gt[test_idx], salmap_pred[test_idx])
# pl.plot(recall_cnn, precision_cnn, 'r', linewidth=2, label='cnn vs. gt')
# pl.xlabel('recall')
# pl.ylabel('precision')
# pl.legend()
# pl.savefig(save_path+'roc.jpg')
# print 'ROC curve saved!'
os.system('xdg-open '+save_path)
